MLS-C01 Data Engineering Practice Question
A data engineer is building a data pipeline using AWS Glue. The pipeline reads data from Amazon S3, transforms it, and writes it back to S3 in a different format. The engineer needs to handle schema evolution (new columns added over time). Which TWO features of AWS Glue can help manage schema evolution?
⚠ Common exam trap
Watch out — candidates often confuse AWS Lake Formation's data lake governance features with schema evolution capabilities, or assume Athena's query-time schema flexibility is equivalent to Glue's ETL-time schema handling.
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
✓
AWS Glue Data Catalog
AWS Glue Data Catalog is correct because it stores schema metadata and can be updated automatically or manually to reflect new columns added to source data, enabling schema evolution tracking. AWS Glue DynamicFrame is correct because it provides a flexible, schema-on-read structure that can accommodate varying schemas across records, allowing transformations to handle new columns without breaking the pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue Data Catalog
Why this is correct
Data Catalog stores schema and can be updated as schema evolves.
- ✓
AWS Glue DynamicFrame
Why this is correct
DynamicFrame can handle schema changes by allowing optional fields.
- ✗
AWS Lake Formation
Why it's wrong here
Lake Formation is for access control and data lake management, not schema evolution.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data but does not manage schema evolution.
- ✗
Amazon S3 object tags
Why it's wrong here
Object tags are metadata, not for schema evolution.
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.