Amazon DVA-C01 AWS Certified Developer – Associate Online Training
Amazon DVA-C01 Online Training
The questions for DVA-C01 were last updated at Mar 26,2024.
- Exam Code: DVA-C01
- Exam Name: AWS Certified Developer – Associate
- Certification Provider: Amazon
- Latest update: Mar 26,2024
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A . METADATA$ACTION
- B . METADATA$FILE_ID
- C . METADATA$ISUPDATE
- D . METADATA$DELETE
- E . METADATA$ROW_ID
Rebuild the environment with the new load balancer type.
Queries to an Amazon DynamoDB table are consuming a large amount of read capacity. The table has a significant number of large attributes. The application does not need all of the attribute data.
How can DynamoDB costs be minimized while maximizing application performance?
- A . Batch all the writes, and perform the write operations when no or few reads are being performed.
- B . Create a global secondary index with a minimum set of projected attributes.
- C . Implement exponential backoffs in the application.
- D . Load balance the reads to the table using an Application Load Balancer.