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Databricks Databricks Certified Associate Developer for Apache Spark 3.0 Databricks Certified Associate Developer for Apache Spark 3.0 exam Online Training
Question #1
Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?
- A . itemsDf.write.avro(fileLocation)
- B . itemsDf.write.format("avro").mode("ignore").save(fileLocation)
- C . itemsDf.write.format("avro").mode("errorifexists").save(fileLocation)
- D . itemsDf.save.format("avro").mode("ignore").write(fileLocation)
- E . spark.DataFrameWriter(itemsDf).format("avro").write(fileLocation)
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #2
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
- A . 1, 10
- B . 1, 8
- C . 10
- D . 7, 9, 10
- E . 1, 4, 6, 9
Question #30
Question #30
Question #32
Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?
- A . transactionsDf.sort(asc(value)).show(10)
- B . transactionsDf.sort(col("value")).show(10)
- C . transactionsDf.sort(col("value").desc()).head()
- D . transactionsDf.sort(col("value").asc()).print(10)
- E . transactionsDf.orderBy("value").asc().show(10)
Question #32
Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?
- A . transactionsDf.sort(asc(value)).show(10)
- B . transactionsDf.sort(col("value")).show(10)
- C . transactionsDf.sort(col("value").desc()).head()
- D . transactionsDf.sort(col("value").asc()).print(10)
- E . transactionsDf.orderBy("value").asc().show(10)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #34
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?
- A . from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
- B . transactionsDf.cache()
- C . transactionsDf.storage_level(‘MEMORY_ONLY’)
- D . transactionsDf.persist()
- E . transactionsDf.clear_persist()
- F . from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #55
spark.read.json(filePath, schema=schema)
C. spark.read.json(filePath, schema=schema_of_json(json_schema))
D. spark.read.json(filePath, schema=spark.read.json(json_schema))
Question #77
"left_semi"
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"left_semi"
Question #98
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Question #120
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Question #121
Which of the following is a viable way to improve Spark’s performance when dealing with large amounts of data, given that there is only a single application running on the cluster?
- A . Increase values for the properties spark.default.parallelism and spark.sql.shuffle.partitions
- B . Decrease values for the properties spark.default.parallelism and spark.sql.partitions
- C . Increase values for the properties spark.sql.parallelism and spark.sql.partitions
- D . Increase values for the properties spark.sql.parallelism and spark.sql.shuffle.partitions
- E . Increase values for the properties spark.dynamicAllocation.maxExecutors, spark.default.parallelism, and spark.sql.shuffle.partitions
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #122
Which of the following is the deepest level in Spark’s execution hierarchy?
- A . Job
- B . Task
- C . Executor
- D . Slot
- E . Stage
Question #135
count()
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #136
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?
- A . tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’), ‘outer’)
- B . transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})
- C . transactionsDf.select(‘value’, ‘productId’).distinct()
- D . transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()
- E . transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #145
+————-+———+—–+——-+———+—-+
- A . The column names should be listed directly as arguments to the operator and not as a list.
- B . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
- C . The select operator should be replaced by a drop operator.
- D . The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
- E . The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Question #166
col("value")
Question #167
Which of the following code blocks returns a DataFrame showing the mean value of column "value" of DataFrame transactionsDf, grouped by its column storeId?
- A . transactionsDf.groupBy(col(storeId).avg())
- B . transactionsDf.groupBy("storeId").avg(col("value"))
- C . transactionsDf.groupBy("storeId").agg(avg("value"))
- D . transactionsDf.groupBy("storeId").agg(average("value"))
- E . transactionsDf.groupBy("value").average()
Question #167
Which of the following code blocks returns a DataFrame showing the mean value of column "value" of DataFrame transactionsDf, grouped by its column storeId?
- A . transactionsDf.groupBy(col(storeId).avg())
- B . transactionsDf.groupBy("storeId").avg(col("value"))
- C . transactionsDf.groupBy("storeId").agg(avg("value"))
- D . transactionsDf.groupBy("storeId").agg(average("value"))
- E . transactionsDf.groupBy("value").average()
Question #169
spark.createDataFrame([("red",), ("blue",), ("green",)], "color")
Instead of calling spark.createDataFrame, just DataFrame should be called.
- A . The commas in the tuples with the colors should be eliminated.
- B . The colors red, blue, and green should be expressed as a simple Python list, and not a list of tuples.
- C . Instead of color, a data type should be specified.
- D . The "color" expression needs to be wrapped in brackets, so it reads ["color"].
Question #169
spark.createDataFrame([("red",), ("blue",), ("green",)], "color")
Instead of calling spark.createDataFrame, just DataFrame should be called.
- A . The commas in the tuples with the colors should be eliminated.
- B . The colors red, blue, and green should be expressed as a simple Python list, and not a list of tuples.
- C . Instead of color, a data type should be specified.
- D . The "color" expression needs to be wrapped in brackets, so it reads ["color"].
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #171
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?
- A . itemsDf.persist(StorageLevel.MEMORY_ONLY)
- B . itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
- C . itemsDf.store()
- D . itemsDf.cache()
- E . itemsDf.write.option(‘destination’, ‘memory’).save()
Question #183
+————-+———+—–+——-+———+—-+
- A . transactionsDf.max(‘value’).min(‘value’)
- B . transactionsDf.agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- C . transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))
- D . transactionsDf.groupby(‘productId’).agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- E . transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})
Question #183
+————-+———+—–+——-+———+—-+
- A . transactionsDf.max(‘value’).min(‘value’)
- B . transactionsDf.agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- C . transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))
- D . transactionsDf.groupby(‘productId’).agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- E . transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})
Question #183
+————-+———+—–+——-+———+—-+
- A . transactionsDf.max(‘value’).min(‘value’)
- B . transactionsDf.agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- C . transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))
- D . transactionsDf.groupby(‘productId’).agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- E . transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})
Question #183
+————-+———+—–+——-+———+—-+
- A . transactionsDf.max(‘value’).min(‘value’)
- B . transactionsDf.agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- C . transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))
- D . transactionsDf.groupby(‘productId’).agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))
- E . transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #187
spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())]))
D. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
E. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #200
articlesDf = articlesDf.groupby("col").count()
B. 4, 5
C. 2, 5, 3
D. 5, 2
E. 2, 3, 4
F. 2, 5, 4
Question #217
"MM d (EEE)"
Question #217
"MM d (EEE)"
Question #217
"MM d (EEE)"
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #220
itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Question #227
importedDf = spark.read.json(jsonPath)
- A . 4, 1, 2
- B . 5, 1, 3
- C . 5, 2
- D . 4, 1, 3
- E . 5, 1, 2
Question #227
importedDf = spark.read.json(jsonPath)
- A . 4, 1, 2
- B . 5, 1, 3
- C . 5, 2
- D . 4, 1, 3
- E . 5, 1, 2
Question #229
Which of the following code blocks returns a copy of DataFrame transactionsDf where the column storeId has been converted to string type?
- A . transactionsDf.withColumn("storeId", convert("storeId", "string"))
- B . transactionsDf.withColumn("storeId", col("storeId", "string"))
- C . transactionsDf.withColumn("storeId", col("storeId").convert("string"))
- D . transactionsDf.withColumn("storeId", col("storeId").cast("string"))
- E . transactionsDf.withColumn("storeId", convert("storeId").as("string"))
Question #230
Which of the following code blocks writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?
- A . itemsDf.write.mode("overwrite").parquet(filePath)
- B . itemsDf.write.option("parquet").mode("overwrite").path(filePath)
- C . itemsDf.write(filePath, mode="overwrite")
- D . itemsDf.write.mode("overwrite").path(filePath)
- E . itemsDf.write().parquet(filePath, mode="overwrite")
Question #230
Which of the following code blocks writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?
- A . itemsDf.write.mode("overwrite").parquet(filePath)
- B . itemsDf.write.option("parquet").mode("overwrite").path(filePath)
- C . itemsDf.write(filePath, mode="overwrite")
- D . itemsDf.write.mode("overwrite").path(filePath)
- E . itemsDf.write().parquet(filePath, mode="overwrite")
Question #232
Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?
- A . Slot is another name for executor.
- B . There must be less executors than tasks.
- C . An executor runs on a single core.
- D . There must be more slots than tasks.
- E . Tasks run in parallel via slots.
Question #233
Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format
month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:
- A . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))
- B . transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))
- C . transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFor matted")
- D . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))
- E . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))
Question #233
Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format
month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:
- A . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))
- B . transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))
- C . transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFor matted")
- D . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))
- E . transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #235
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column
storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.
- A . The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
- B . Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
- C . Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
- D . partitionOn("storeId") should be called before the write operation.
- E . The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #250
spark.sql(statement).drop("value", "storeId", "attributes")
Question #260
transactionsDf.withColumn("result", evaluateTestSuccess(col("storeId")))
Question #261
Which of the following statements about broadcast variables is correct?
- A . Broadcast variables are serialized with every single task.
- B . Broadcast variables are commonly used for tables that do not fit into memory.
- C . Broadcast variables are immutable.
- D . Broadcast variables are occasionally dynamically updated on a per-task basis.
- E . Broadcast variables are local to the worker node and not shared across the cluster.
Question #262
The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.
Code block:
transactionsDf.filter(col(‘predError’).in([3, 6])).count()
- A . The number of rows cannot be determined with the count() operator.
- B . Instead of filter, the select method should be used.
- C . The method used on column predError is incorrect.
- D . Instead of a list, the values need to be passed as single arguments to the in operator.
- E . Numbers 3 and 6 need to be passed as string variables.
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #263
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?
- A . transactionsDf.drop(["predError", "value"])
- B . transactionsDf.drop("predError", "value")
- C . transactionsDf.drop(col("predError"), col("value"))
- D . transactionsDf.drop(predError, value)
- E . transactionsDf.drop("predError & value")
Question #275
spark.read.options("modifiedBefore", "2029-03-
20T05:44:46").schema(schema).load(filePath)
- A . The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark’s DataFrameReader is incorrect.
- B . Columns in the schema definition use the wrong object type and the syntax of the call to Spark’s DataFrameReader is incorrect.
- C . The data type of the schema is incompatible with the schema() operator and the modification date threshold is specified incorrectly.
- D . Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.
- E . Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.
Question #276
Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?
- A . itemsDf.cache().count()
- B . itemsDf.cache(eager=True)
- C . cache(itemsDf)
- D . itemsDf.cache().filter()
- E . itemsDf.rdd.storeCopy()
Question #277
The code block displayed below contains an error. The code block is intended to join
DataFrame itemsDf with the larger DataFrame transactionsDf on column itemId. Find the error.
Code block:
transactionsDf.join(itemsDf, "itemId", how="broadcast")
- A . The syntax is wrong, how= should be removed from the code block.
- B . The join method should be replaced by the broadcast method.
- C . Spark will only perform the broadcast operation if this behavior has been enabled on the Spark cluster.
- D . The larger DataFrame transactionsDf is being broadcasted, rather than the smaller DataFrame itemsDf.
- E . broadcast is not a valid join type.
Question #277
The code block displayed below contains an error. The code block is intended to join
DataFrame itemsDf with the larger DataFrame transactionsDf on column itemId. Find the error.
Code block:
transactionsDf.join(itemsDf, "itemId", how="broadcast")
- A . The syntax is wrong, how= should be removed from the code block.
- B . The join method should be replaced by the broadcast method.
- C . Spark will only perform the broadcast operation if this behavior has been enabled on the Spark cluster.
- D . The larger DataFrame transactionsDf is being broadcasted, rather than the smaller DataFrame itemsDf.
- E . broadcast is not a valid join type.
Question #279
print(itemsDf.types)
B. itemsDf.printSchema()
C. spark.schema(itemsDf)
D. itemsDf.rdd.printSchema()
E. itemsDf.print.schema()
Question #279
print(itemsDf.types)
B. itemsDf.printSchema()
C. spark.schema(itemsDf)
D. itemsDf.rdd.printSchema()
E. itemsDf.print.schema()
Question #281
The code block displayed below contains an error. The code block should return the average of rows in column value grouped by unique storeId. Find the error.
Code block:
transactionsDf.agg("storeId").avg("value")
- A . Instead of avg("value"), avg(col("value")) should be used.
- B . The avg("value") should be specified as a second argument to agg() instead of being appended to it.
- C . All column names should be wrapped in col() operators.
- D . agg should be replaced by groupBy.
- E . "storeId" and "value" should be swapped.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #282
Which of the following statements about the differences between actions and transformations is correct?
- A . Actions are evaluated lazily, while transformations are not evaluated lazily.
- B . Actions generate RDDs, while transformations do not.
- C . Actions do not send results to the driver, while transformations do.
- D . Actions can be queued for delayed execution, while transformations can only be processed immediately.
- E . Actions can trigger Adaptive Query Execution, while transformation cannot.
Question #290
+——+———————————-+——————-+
Code block:
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
- A . All column names need to be wrapped in the col() operator.
- B . Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument "," needs to be passed to the split method.
- C . Operator withColumnRenamed needs to be replaced with operator withColumn and the split method needs to be replaced by the splitString method.
- D . Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument " " needs to be passed to the split method.
- E . The expressions "itemNameElements" and split("itemName") need to be swapped.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #291
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
- A . The arguments to the withColumn method need to be reordered.
- B . The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
- C . The copy() operator should be appended to the code block to ensure a copy is returned.
- D . Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
- E . The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
Question #343
transactionsDf.select(count_to_target_udf(‘predError’))
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #344
Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?
- A . transactionsDf.repartition(transactionsDf.getNumPartitions()+2)
- B . transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)
- C . transactionsDf.coalesce(10)
- D . transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)
- E . transactionsDf.repartition(transactionsDf._partitions+2)
Question #352
part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-
c000.csv.gz
spark.option("header",True).csv(filePath)
- A . spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)
- B . spark.read().option("header",True).load(filePath)
- C . spark.read.format("csv").option("header",True).load(filePath)
- D . spark.read.load(filePath)
Question #353
Which of the following statements about stages is correct?
- A . Different stages in a job may be executed in parallel.
- B . Stages consist of one or more jobs.
- C . Stages ephemerally store transactions, before they are committed through actions.
- D . Tasks in a stage may be executed by multiple machines at the same time.
- E . Stages may contain multiple actions, narrow, and wide transformations.
Question #354
Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a
DataFrame?
- A . spark.mode("parquet").read("/FileStore/imports.parquet")
- B . spark.read.path("/FileStore/imports.parquet", source="parquet")
- C . spark.read().parquet("/FileStore/imports.parquet")
- D . spark.read.parquet("/FileStore/imports.parquet")
- E . spark.read().format(‘parquet’).open("/FileStore/imports.parquet")
Question #354
Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a
DataFrame?
- A . spark.mode("parquet").read("/FileStore/imports.parquet")
- B . spark.read.path("/FileStore/imports.parquet", source="parquet")
- C . spark.read().parquet("/FileStore/imports.parquet")
- D . spark.read.parquet("/FileStore/imports.parquet")
- E . spark.read().format(‘parquet’).open("/FileStore/imports.parquet")
Question #356
Which of the following describes the difference between client and cluster execution modes?
- A . In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.
- B . In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
- C . In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
- D . In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
- E . In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
Question #357
Which of the following statements about Spark’s configuration properties is incorrect?
- A . The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.
- B . The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.
- C . The default value for spark.sql.autoBroadcastJoinThreshold is 10MB.
- D . The default number of partitions to use when shuffling data for joins or aggregations is 300.
- E . The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.
Question #358
Which of the following describes a valid concern about partitioning?
- A . A shuffle operation returns 200 partitions if not explicitly set.
- B . Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
- C . No data is exchanged between executors when coalesce() is run.
- D . Short partition processing times are indicative of low skew.
- E . The coalesce() method should be used to increase the number of partitions.
Question #359
Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?
- A . transactionsDf.filter(productId==3 or productId<1)
- B . transactionsDf.filter((col("productId")==3) or (col("productId")<1))
- C . transactionsDf.filter(col("productId")==3 | col("productId")<1)
- D . transactionsDf.where("productId"=3).or("productId"<1))
- E . transactionsDf.filter((col("productId")==3) | (col("productId")<1))
Question #359
Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?
- A . transactionsDf.filter(productId==3 or productId<1)
- B . transactionsDf.filter((col("productId")==3) or (col("productId")<1))
- C . transactionsDf.filter(col("productId")==3 | col("productId")<1)
- D . transactionsDf.where("productId"=3).or("productId"<1))
- E . transactionsDf.filter((col("productId")==3) | (col("productId")<1))
Question #361
Which of the following describes a difference between Spark’s cluster and client execution modes?
- A . In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.
- B . In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.
- C . In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.
- D . In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.
- E . In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.
Question #362
Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?
- A . spark.read.json(filePath)
- B . spark.read.path(filePath, source="json")
- C . spark.read().path(filePath)
- D . spark.read().json(filePath)
- E . spark.read.path(filePath)
Question #362
Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?
- A . spark.read.json(filePath)
- B . spark.read.path(filePath, source="json")
- C . spark.read().path(filePath)
- D . spark.read().json(filePath)
- E . spark.read.path(filePath)
Question #364
The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by
column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items
in column value. Find the error.
Code block:
transactionsDf.orderBy(‘value’, asc_nulls_first(col(‘predError’)))
- A . Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement.
- B . Column value should be wrapped by the col() operator.
- C . Column predError should be sorted in a descending way, putting nulls last.
- D . Column predError should be sorted by desc_nulls_first() instead.
- E . Instead of orderBy, sort should be used.
Question #365
Which of the following code blocks removes all rows in the 6-column DataFrame transactionsDf that have missing data in at least 3 columns?
- A . transactionsDf.dropna("any")
- B . transactionsDf.dropna(thresh=4)
- C . transactionsDf.drop.na("",2)
- D . transactionsDf.dropna(thresh=2)
- E . transactionsDf.dropna("",4)
Question #365
Which of the following code blocks removes all rows in the 6-column DataFrame transactionsDf that have missing data in at least 3 columns?
- A . transactionsDf.dropna("any")
- B . transactionsDf.dropna(thresh=4)
- C . transactionsDf.drop.na("",2)
- D . transactionsDf.dropna(thresh=2)
- E . transactionsDf.dropna("",4)
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #367
Which of the following describes tasks?
- A . A task is a command sent from the driver to the executors in response to a transformation.
- B . Tasks transform jobs into DAGs.
- C . A task is a collection of slots.
- D . A task is a collection of rows.
- E . Tasks get assigned to the executors by the driver.
Question #383
Question #383
Question #385
Which of the following is a problem with using accumulators?
- A . Only unnamed accumulators can be inspected in the Spark UI.
- B . Only numeric values can be used in accumulators.
- C . Accumulator values can only be read by the driver, but not by executors.
- D . Accumulators do not obey lazy evaluation.
- E . Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
Question #386
The code block displayed below contains an error. The code block should combine data from DataFrames itemsDf and transactionsDf, showing all rows of DataFrame itemsDf that have a matching value in column itemId with a value in column transactionsId of DataFrame transactionsDf.
Find the error.
Code block:
itemsDf.join(itemsDf.itemId==transactionsDf.transactionId)
- A . The join statement is incomplete.
- B . The union method should be used instead of join.
- C . The join method is inappropriate.
- D . The merge method should be used instead of join.
- E . The join expression is malformed.
Question #387
Which of the following statements about lazy evaluation is incorrect?
- A . Predicate pushdown is a feature resulting from lazy evaluation.
- B . Execution is triggered by transformations.
- C . Spark will fail a job only during execution, but not during definition.
- D . Accumulators do not change the lazy evaluation model of Spark.
- E . Lineages allow Spark to coalesce transformations into stages
Question #388
Which of the following are valid execution modes?
- A . Kubernetes, Local, Client
- B . Client, Cluster, Local
- C . Server, Standalone, Client
- D . Cluster, Server, Local
- E . Standalone, Client, Cluster
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #389
Which of the following describes characteristics of the Dataset API?
- A . The Dataset API does not support unstructured data.
- B . In Python, the Dataset API mainly resembles Pandas’ DataFrame API.
- C . In Python, the Dataset API’s schema is constructed via type hints.
- D . The Dataset API is available in Scala, but it is not available in Python.
- E . The Dataset API does not provide compile-time type safety.
Question #397
+——+—————————–+——————-+
- A . itemsDf.withColumn(‘attributes’, sort_array(col(‘attributes’).desc()))
- B . itemsDf.withColumn(‘attributes’, sort_array(desc(‘attributes’)))
- C . itemsDf.withColumn(‘attributes’, sort(col(‘attributes’), asc=False))
- D . itemsDf.withColumn("attributes", sort_array("attributes", asc=False))
- E . itemsDf.select(sort_array("attributes"))
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #398
Which is the highest level in Spark’s execution hierarchy?
- A . Task
- B . Executor
- C . Slot
- D . Job
- E . Stage
Question #410
spark.sql ("FROM transactionsDf SELECT predError, value WHERE transactionId % 2 = 2")
F. transactionsDf.filter(col(transactionId).isin([3,4,6]))
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #411
Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
- A . Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
- B . Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
- C . Use a narrow transformation to reduce the number of partitions.
- D . Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Question #417
col(["transactionId", "predError", "value", "f"])
Question #417
col(["transactionId", "predError", "value", "f"])
Question #417
col(["transactionId", "predError", "value", "f"])
Question #417
col(["transactionId", "predError", "value", "f"])
Question #417
col(["transactionId", "predError", "value", "f"])
Question #417
col(["transactionId", "predError", "value", "f"])
Question #423
dfDates = dfDates.withColumnRenamed("date", to_datetime("date", "yyyy-MM-dd HH:mm:ss"))
E. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
Question #424
Which of the following is a characteristic of the cluster manager?
- A . Each cluster manager works on a single partition of data.
- B . The cluster manager receives input from the driver through the SparkContext.
- C . The cluster manager does not exist in standalone mode.
- D . The cluster manager transforms jobs into DAGs.
- E . In client mode, the cluster manager runs on the edge node.
Question #425
Which of the following statements about DAGs is correct?
- A . DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.
- B . DAG stands for "Directing Acyclic Graph".
- C . Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.
- D . In contrast to transformations, DAGs are never lazily executed.
- E . DAGs can be decomposed into tasks that are executed in parallel.
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