AWS Certified Data Engineer Associate DEA-C01 Practice Question
A retailer stores clickstream data as Parquet files in Amazon S3. Analysts query the data with Amazon Athena several times a day, and weekly batch jobs update or delete late-arriving records. The company uses AWS Lake Formation and must enforce row-level security while supporting ACID transactions with the least administration. Which approach meets these requirements?
Convert the dataset to a Lake Formation governed table and use LF tag-based policies to grant analysts SELECT access with row filters.
Create an external table in the AWS Glue Data Catalog and control access only with S3 bucket policies and Athena workgroup-level data filters.
Load the data into an Amazon Redshift cluster and share secure views through Lake Formation for row-level access.
Enable object-level ACLs on the S3 bucket and restrict rows by forcing analysts to use Athena views containing WHERE clauses.
Lake Formation governed tables add ACID compliance, automatic compaction, and concurrency controls on S3 objects. When a dataset is registered as a governed table, Lake Formation can apply fine-grained permissions-including row and column filters-through LF tag-based access control. Analysts receive SELECT on the governed table, and update jobs can use the transactional writes that governed tables provide. Merely using bucket policies, Athena workgroups, or object ACLs does not give row-level security or ACID semantics. Importing the data into Amazon Redshift is unnecessary overhead and Redshift sharing via Lake Formation does not cover S3 data updates.
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What are Lake Formation governed tables?
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How do LF tag-based permissions work?
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Why are ACID transactions important for S3 datasets?
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What are Lake Formation governed tables?
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What are LF tag-based access control policies?
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How does Amazon Athena integrate with Lake Formation governed tables?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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