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MLA-C01 Practice Question: A data engineer is designing an ETL pipeline…
A data engineer is designing an ETL pipeline using AWS Glue to transform raw data from S3 into a curated set for ML training. The data contains personally identifiable information (PII) that must be masked before being used by data scientists. Which TWO actions should the engineer take? (Choose TWO.)
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 AWS Glue DataBrew to define PII masking transformations
AWS Glue ETL jobs support custom transforms via PySpark. DataBrew provides a visual interface for data preparation including PII masking. The Glue Data Catalog is for metadata, not transformation. Crawlers catalog data, not mask. Kinesis Firehose is for streaming, not batch ETL.
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 AWS Glue DataBrew to define PII masking transformations
Why this is correct
DataBrew provides built-in transforms for PII detection and masking.
- ✗
Use Amazon Kinesis Data Firehose to transform data at ingestion
Why it's wrong here
Firehose is for streaming, not batch ETL from S3.
- ✗
Use AWS Glue Data Catalog to automatically mask PII fields
Why it's wrong here
Data Catalog is a metadata store; it does not perform data transformations.
- ✓
Use AWS Glue ETL scripts with PySpark to apply custom masking functions
Why this is correct
Glue ETL can run custom PySpark code to mask PII columns.
- ✗
Use AWS Glue Crawler to detect and mask PII automatically
Why it's wrong here
Crawlers infer schema and populate the catalog; they do not mask data.
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 |
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.