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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A media company stores terabytes of Parquet files in an Amazon S3 data lake, which is queried daily by Amazon Athena and Amazon Redshift Spectrum. New regulations require that only approved columns and specific rows be visible to different analytics teams. Additionally, updates to the data must be transactionally consistent. The company wants a managed solution using open table formats that minimizes both ongoing maintenance of IAM policies and data duplication. Which solution best meets these requirements?

  • Convert the S3 location to an AWS Lake Formation governed table and grant the analytics teams access through Lake Formation permissions to enforce transactional consistency and access control.

  • Use an AWS Glue ETL job to convert the data to Apache Iceberg format in Amazon S3. Define row and column filters in AWS Lake Formation and grant the analytics teams access through Lake Formation permissions.

  • Load the data into Amazon Redshift tables. Create distinct schemas with column-level privileges for each team and use federated queries to access any remaining S3 data.

  • Use AWS Glue ETL jobs to create separate S3 copies of the data for each team, removing unauthorized columns, and restrict access to each copy using S3 bucket policies.

AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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