A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to…

Question

Options

• ACreate a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
• BCreate Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection
• CCreate Apache Spark code to run on an AWS Glue job. Program the code to perform a regex
• DCreate Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an

• A
  7% (2)
• B
  81% (22)
• C
  7% (2)
• D
  4% (1)

Explanation

Option B is correct because AWS Glue provides a native Sensitive Data Detection transform that can automatically identify and mask PII like credit card numbers without requiring custom code - you configure it declaratively, making it the lowest-effort solution for a data pipeline already reading from S3.

Why the distractors are wrong:

• Option A (Macie): Macie is a discovery and alerting service - it finds sensitive data and reports it, but it cannot mask or transform the data in-place. It's the wrong tool for a transformation requirement.
• Option C (Glue + custom regex): While Glue is the right service, writing your own regex for credit card detection is redundant when the built-in Sensitive Data Detection transform already does this - more development effort for the same result.
• Option D (EC2 + custom regex): Using EC2 requires provisioning, managing infrastructure, and writing custom masking logic - the highest development effort of all options.

Memory tip: Think of it this way - Macie detects, Glue transforms. When you need to act on sensitive data (mask, redact, replace), you want a Glue job with its built-in PII transforms. When you just need to know sensitive data exists, Macie is your tool.

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