AWS Certified Data Engineer Associate DEA-C01 Practice Question
A retail company stores clickstream records in Amazon S3 using the prefix structure s3://bucket/events/year=YYYY/month=MM/day=DD/hour=HH/. An AWS Glue Data Catalog table exposes the data to Amazon Athena. Hundreds of new hour-level partitions arrive each day, and analysts must query the most recent data within minutes while keeping maintenance cost low. Which solution best meets these requirements?
Enable partition projection on the Glue Data Catalog table and define templates for year, month, day, and hour.
Configure Amazon S3 event notifications to trigger an AWS Lambda function that calls BatchCreatePartition for every new object.
Instruct analysts to execute MSCK REPAIR TABLE before each Athena query to refresh partition metadata.
Schedule an AWS Glue crawler to run every 5 minutes to discover and add new partitions.
Enabling partition projection on the AWS Glue Data Catalog table lets Athena derive partition values from the S3 prefix pattern at query runtime. No crawler, DDL statement, or Lambda function is required, so new partitions are available immediately with virtually no additional cost. Running a Glue crawler every few minutes or invoking BatchCreatePartition on every object adds unnecessary expense. Requiring analysts to run MSCK REPAIR TABLE introduces manual effort and delays.
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Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is partition projection in AWS Glue?
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How does MSCK REPAIR TABLE work in Athena?
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What are the limitations of using AWS Glue crawlers for frequent partition updates?
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What is partition projection in AWS Glue?
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Why is running an AWS Glue crawler every 5 minutes not ideal for this scenario?
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How does the MSCK REPAIR TABLE command work, and why is it not an optimal solution here?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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