AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data lake on Amazon S3 contains a raw table with customer email addresses. Compliance requires downstream analytics to receive a deterministic pseudonym for each address so that joins are possible, while the original email can never be inferred without an internal secret key. As the data engineer, which solution most simply applies a keyed salt during anonymization by relying only on managed services?
Enable S3 Bucket Keys with SSE-KMS and configure an S3 Object Lambda access point to rewrite objects on the fly.
Use server-side encryption with customer-provided keys (SSE-C) on the raw bucket and rotate the keys daily.
Create an AWS Glue DataBrew recipe that applies the HMAC-SHA256 transformation to the email column using a secret key retrieved from AWS Secrets Manager, then write the output to a curated S3 prefix.
Deploy an AWS Lambda function triggered by S3 PUT to read each object, prepend a random value to every email before hashing, store the mapping in Amazon DynamoDB, and write the redacted file back to S3.
AWS Glue DataBrew includes a built-in HMAC-SHA256 transformation that hashes a column by combining the field value with a secret key. The key can be stored in AWS Secrets Manager and referenced directly from the recipe. The result is a consistent, non-reversible pseudonym that supports deterministic joins. S3 bucket keys, SSE-C, or Lambda code either do not perform deterministic hashing or require custom code and state management, making them less suitable.
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AWS Certified Data Engineer Associate DEA-C01
Data Security and Governance
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