MLS-C01 Question #293: Real Exam Question with Answer & Explanation

Question

A company hosts a machine learning (ML) dataset repository on Amazon S3. A data scientist is preparing the repository to train a model. The data scientist needs to redact personally identifiable information (PH) from the dataset. Which solution will meet these requirements with the LEAST development effort?

Options

• AUse Amazon SageMaker Data Wrangler with a custom transformation to identify and redact the
• BCreate a custom AWS Lambda function to read the files, identify the PII. and redact the PII
• CUse AWS Glue DataBrew to identity and redact the PII
• DUse an AWS Glue development endpoint to implement the PII redaction from within a notebook

Explanation

{"question_number": 1, "question_summary": "Redact PII from an ML dataset in S3 with least development effort.", "correct_answer": "C", "explanation": "AWS Glue DataBrew is a visual data preparation tool that includes built-in transformations specifically for detecting and redacting PII (such as names, emails, SSNs, and phone numbers) without writing any custom code. You simply configure the PII detection transform in the DataBrew recipe and apply it to the S3 dataset. Option A (Data Wrangler with a custom transformation) and B (custom Lambda function) both require custom development effort. Option D (Glue development endpoint with a notebook) also requires writing PII logic manually. DataBrew's native PII handling makes it the lowest-effort solution.", "generated_by": "claude-sonnet", "llm_judge_score": 3}

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