DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data pipeline that uses AWS Glue to transform data stored in Amazon S3. The transformation logic must be written in Python and should handle schema evolution automatically. Which THREE features or configurations should the engineer use? (Select THREE.)
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 `applyMapping` transformations
Correct options: B, D, E. AWS Glue DynamicFrames (E) handle schema evolution automatically by allowing schema on read and accommodating changes in data structure. Schema detection in the Glue job (D) enables the job to infer the schema from the data, which is essential for handling evolving schemas. Using `applyMapping` (B) provides explicit control over schema transformations and can be combined with DynamicFrames to manage schema changes. Option A (scheduling a Glue crawler) is meant for updating the Data Catalog, not for within-job schema evolution. Option C (Spark SQL) does not inherently handle schema evolution; it relies on static schemas.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule a Glue crawler to update the schema
Why it's wrong here
Crawlers are for cataloging, not for transformation logic.
- ✓
Use `applyMapping` transformations
Why this is correct
Facilitates schema manipulation.
- ✗
Use Spark SQL for transformations
Why it's wrong here
Does not handle schema evolution automatically.
- ✓
Enable schema detection in the Glue job
Why this is correct
Allows automatic schema inference.
- ✓
Use DynamicFrames instead of DataFrames
Why this is correct
DynamicFrames support 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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