MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
A common mix-up: candidates choose SageMaker Processing (Option D) because it is a SageMaker-native service, but the question asks for the 'most efficient' approach for standalone data anonymization, and AWS Glue is the correct serverless ETL service for this task, not a processing job tied to the training pipeline.
Answer choices
Why each option matters
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.
AWS Glue ETL jobs are purpose-built for serverless data transformation at scale, making them the most efficient choice for anonymizing PII in S3 before training. Glue can read directly from S3, apply built-in or custom anonymization transforms (e.g., masking, hashing) using PySpark or Scala, and write the cleaned data to a separate S3 bucket without provisioning any infrastructure. This approach decouples the data preparation from SageMaker, avoids unnecessary compute costs during training, and scales automatically with data volume.
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.
- ✗
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.