DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon S3 to store raw data and AWS Glue to run ETL jobs that transform the data into analytics-ready tables. The Glue job reads from a source with a schema that changes frequently (new columns added). The engineer wants the Glue job to automatically adapt to schema changes without manual intervention. Which configuration should the engineer use?
⚠ Common exam trap
Candidates often confuse schema-on-read (Parquet's flexibility) with automatic schema evolution in the Data Catalog, leading them to choose Option B, but schema-on-read does not update the target table's metadata, which is required for downstream analytics tools to query the new columns.
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
✓
Enable the 'Update schema' option in the Glue job's output target configuration.
Enabling the 'Update schema' option in the Glue job's output target configuration allows the job to automatically add new columns to the target table in the Data Catalog when the source schema changes. This setting directly addresses the requirement for automatic adaptation to schema changes without manual intervention, as it updates the table definition during the ETL job run.
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 run after each ETL job to update the Data Catalog.
Why it's wrong here
This adds extra time and cost, and does not adapt the job itself.
- ✗
Set the job to use schema-on-read by storing data in Parquet format.
Why it's wrong here
Schema-on-read allows querying without predefined schema but does not adapt the ETL transformation.
- ✓
Enable the 'Update schema' option in the Glue job's output target configuration.
Why this is correct
This option automatically adds new columns to the target table.
- ✗
Use Glue's partition indexes to automatically detect new columns.
Why it's wrong here
Partition indexes are for partition pruning, not 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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