AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests customer orders into an Amazon S3 data lake. Compliance rules state that the first six digits of any credit-card number must be replaced with the letter X before business analysts can query the data with Amazon Athena. The solution must run automatically every night, store the sanitized results in a separate S3 prefix, and avoid writing or maintaining custom code. Which approach meets these requirements?
Enable Amazon Macie sensitive data discovery on the bucket and configure Macie to automatically redact detected credit-card numbers before analysts access the data.
Use AWS Lake Formation column-level security to hide the credit-card column and grant analysts access to a masked view of that column.
Create an AWS Glue DataBrew project with a recipe that masks the first six digits of the credit-card column, then schedule a DataBrew job to write the output to a sanitized S3 prefix.
Configure server-side encryption with an AWS KMS key on the S3 bucket so that analysts access only encrypted objects through an S3 Access Point.
AWS Glue DataBrew provides no-code, visual data preparation. Its built-in transformation recipe can locate credit-card values and apply a "Replace with pattern" or "Mask PII" step that substitutes the first six digits with X. A DataBrew job can be scheduled to run nightly and write the transformed dataset to a different Amazon S3 prefix, satisfying automation and separation requirements without custom code.
Amazon Macie can discover sensitive data but does not transform or persist masked copies of the objects. AWS Lake Formation column-level security restricts access but does not modify the underlying values. Enabling server-side encryption with AWS KMS protects data at rest but does not mask credit-card numbers.
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AWS Certified Data Engineer Associate DEA-C01
Data Security and Governance
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