The answer is to use an AWS Glue ETL job to read from S3, apply PII anonymization, and write to another S3 bucket. This is correct because AWS Glue provides a serverless, scalable environment for running custom transformation logic—such as masking, tokenizing, or redacting personally identifiable information—directly on data stored in S3 before it reaches SageMaker. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of the most efficient data preparation pipeline, often contrasting Glue’s batch ETL capabilities with SageMaker Processing jobs (which are better for feature engineering, not raw data anonymization) and query-only services like Athena or Redshift Spectrum. A common trap is assuming SageMaker itself handles anonymization, but the exam emphasizes that pre-processing should happen in a dedicated ETL layer. Memory tip: “Glue masks the mess before SageMaker learns the lesson.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using Amazon SageMaker to train machine learning models. The training data is stored in Amazon S3, but the data includes personally identifiable information (PII) that must be anonymized before training. What is the most efficient way to anonymize the data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.
Option B is correct because AWS Glue can run a transformation job to anonymize PII before training. Option A is wrong because SageMaker Processing jobs are for feature engineering, not data anonymization from S3. Option C is wrong because Athena is for querying, not transforming. Option D is wrong because Redshift Spectrum queries data in S3 but does not anonymize efficiently.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.
Why this is correct
Glue is a serverless ETL service that can efficiently transform large datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Athena to query the data and apply anonymization functions.
Why it's wrong here
Athena is for querying, not transforming large datasets.
✗
Use Amazon Redshift Spectrum to query and anonymize data in S3.
Why it's wrong here
Redshift Spectrum is for querying, not ETL transformations.
✗
Use a SageMaker Processing job to read from S3 and apply anonymization.
Why it's wrong here
Processing jobs are better for feature engineering, but Glue is more cost-effective for large-scale ETL.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket. — Option B is correct because AWS Glue can run a transformation job to anonymize PII before training. Option A is wrong because SageMaker Processing jobs are for feature engineering, not data anonymization from S3. Option C is wrong because Athena is for querying, not transforming. Option D is wrong because Redshift Spectrum queries data in S3 but does not anonymize efficiently.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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