AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your ecommerce company stores daily order data as Parquet files in Amazon S3 under the prefix s3://sales-data/orders/year=YYYY/month=MM/day=DD/. A Lambda function, triggered every 15 minutes by Amazon EventBridge, submits Amazon Athena queries that must include the most recent files as soon as they arrive. The team wants to minimize query latency and eliminate the operational cost of running AWS Glue crawlers or the MSCK REPAIR TABLE command after each file delivery. Which approach best meets these requirements?
Modify the Lambda function to run the statement MSCK REPAIR TABLE orders before every query submission to refresh partition metadata.
Schedule an AWS Glue crawler to run every 15 minutes so that new partitions are added to the Data Catalog before each query executes.
Enable partition projection on the Athena table and specify the year, month, and day ranges; keep the partition columns in the WHERE clause of each query.
Create a new unpartitioned table with a CREATE TABLE AS SELECT (CTAS) statement and query the consolidated data instead of the partitioned source.
Partition projection lets Athena derive partition values from object paths at run time, so Athena does not need to store a separate metadata row for every partition. By enabling partition projection on the orders table and defining the year, month, and day ranges, new partitions become instantly queryable without invoking Glue crawlers or MSCK REPAIR TABLE. This reduces latency and catalog overhead. Scheduling a Glue crawler or running MSCK REPAIR TABLE would achieve correctness but adds cost and delay. Using an unpartitioned CTAS table removes the need for partition updates but would scan far more data and raise query costs.
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What is partition projection in Athena?
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Why does partition projection reduce query latency in Athena?
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How does partition projection compare to AWS Glue crawlers for managing partitions?
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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