AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company stores 100 TB of Parquet files in Amazon S3. Multiple analytics teams need millisecond-latency access with Amazon Athena and Amazon Redshift Spectrum. Security requires row-level and column-level restrictions without adding new data copies. Which approach meets requirements with the lowest operational overhead and no additional storage cost?
Copy each dataset into separate S3 prefixes per team and restrict Amazon Athena workgroups to those prefixes with bucket policies.
Register the S3 locations in AWS Lake Formation and grant row- and column-level Lake Formation permissions that are respected by Athena and Redshift Spectrum.
Use S3 Object Lambda to dynamically remove unauthorized rows and columns when Athena or Redshift Spectrum access the objects.
Load the data into an Amazon Redshift RA3 cluster and use column-level security plus materialized views to filter rows for each team.
AWS Lake Formation can register the existing S3 locations as a governed data lake and apply fine-grained permissions down to row and column level. Athena and Redshift Spectrum are Lake Formation-aware, so the same policies are enforced automatically without additional data movement. Loading the data into a dedicated Redshift cluster, duplicating objects into separate prefixes, or trying to filter with S3 Object Lambda would either create extra storage cost, higher administration effort, or are not supported for query engines. Therefore, using Lake Formation provides the simplest, cost-effective solution that meets the security and performance requirements.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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