Amazon DAS-C01 AWS Certified Data Analytics – Specialty Online Training
Amazon DAS-C01 Online Training
The questions for DAS-C01 were last updated at Feb 28,2026.
- Exam Code: DAS-C01
- Exam Name: AWS Certified Data Analytics - Specialty
- Certification Provider: Amazon
- Latest update: Feb 28,2026
A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?
- A . Migrate the validation process to Amazon Kinesis Data Firehose.
- B . Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
- C . Increase the number of shards in the stream.
- D . Configure multiple Lambda functions to process the stream.
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)
- A . Increase the Kinesis Data Streams retention period to reduce throttling.
- B . Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
- C . Increase the number of shards in the stream using the UpdateShardCount API.
- D . Choose partition keys in a way that results in a uniform record distribution across shards.
- E . Customize the application code to include retry logic to improve performance.
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?
- A . Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.
- B . Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
- C . Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
- D . Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.
A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)
- A . Use the COPY command with the manifest file to load data into Amazon Redshift.
- B . Use S3DistCp to load files into Amazon Redshift.
- C . Use temporary staging tables during the loading process.
- D . Use the UNLOAD command to upload data into Amazon Redshift.
- E . Use Amazon Redshift Spectrum to query files from Amazon S3.
A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.
Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)
- A . Store the data in Apache Avro format using Snappy compression.
- B . Partition the data by year, month, and day.
- C . Store the data in Apache ORC format using no compression.
- D . Store the data in Apache Parquet format using Snappy compression.
- E . Partition the data by sensor, year, month, and day.
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?
- A . Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
- B . Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
- C . Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
- D . Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.
A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.
Which solution meets these requirements?
- A . Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0.
and the default encryption settings. - B . Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
- C . Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory.
Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS. - D . Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory.
Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.
Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)
- A . Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
- B . Use an S3 bucket in the same account as Athena.
- C . Compress the objects to reduce the data transfer I/O.
- D . Use an S3 bucket in the same Region as Athena.
- E . Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
- F . Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.
A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.
How can these requirements be met?
- A . Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.
- B . Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.
- C . Create a customer master key (CMK) in AWS KMS. Create an AWS Lambda function to encrypt and
decrypt the data. Set the KMS key ID in the function’s environment variables. - D . Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data
Streams.
A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs.
The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?
- A . Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
- B . Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.
- C . Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
- D . Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in Amazon S3.
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