AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team stores application logs in an Amazon S3 bucket using keys like s3://company-logs/year=2024/month=04/day=15/*. They register the dataset in the AWS Glue Data Catalog so analysts can query the logs with Amazon Athena and limit scans to the last few days. Which Data Catalog metadata component must they create to enable this partition pruning?
Add partition entries to the Data Catalog table for every year, month, and day prefix in the bucket.
Define a custom crawler classifier that recognizes the year, month, and day parameters in the object keys.
Configure an AWS Glue connection object that points to the S3 bucket's partitioned prefixes.
Create a separate AWS Glue database for each day and direct Athena to the appropriate database.
Athena skips reading objects outside the requested date range only when the underlying AWS Glue table contains partition entries that map each year=YYYY/month=MM/day=DD prefix to its S3 location. These partition metadata records are part of the table definition and tell Athena which S3 objects belong to each partition value. Creating additional databases, classifiers, or connection objects does not inform Athena of the individual partitions and therefore will not enable partition pruning.
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What is partition pruning in Athena?
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How do you add partitions to an AWS Glue Data Catalog table?
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Why doesn’t Athena use custom classifiers or connections to enable partition pruning?
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What is partition pruning in Athena?
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How do you add partitions to an AWS Glue Data Catalog table?
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Why does Athena require partitions in the AWS Glue Data Catalog for efficient querying?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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