AWS Certified Data Engineer – Associate (DEA-C01) — Question 18

A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

Answer options

• A. Create an AWS Glue partition index. Enable partition filtering.
• B. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.
• C. Use Athena partition projection based on the S3 bucket prefix.
• D. Transform the data that is in the S3 bucket to Apache Parquet format.
• E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

Correct answer: A, C

Explanation

Creating an AWS Glue partition index and enabling partition filtering helps to reduce the amount of data scanned during queries, improving performance. Similarly, using Athena partition projection allows for efficient query planning by reducing the number of partitions that need to be considered. The other options, while potentially beneficial in other contexts, do not specifically address the issue of partitioning that is causing the bottleneck.