DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to process JSON logs from S3. The logs have a nested structure and the schema evolves over time. The data engineer needs to ensure the Glue job can handle schema changes without failing. Which configuration should be used?
⚠ Common exam trap
Many candidates confuse the AWS Glue Schema Registry (which enforces schema compatibility and versioning) with the schema evolution capabilities of the Glue DynamicFrame, leading them to choose Option D even though it would reject schema changes rather than adapt to them.
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
✓
Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true
Setting '--enable-glue-datacatalog' allows the Glue job to use the Data Catalog as the metastore, and '--mergeDynamicColumns' (or the equivalent '--enable-schema-evolution' in newer Glue versions) instructs the job to dynamically merge new columns from the evolving JSON schema into the existing table schema during runtime, preventing job failures due to schema mismatches. This is specifically designed for nested, schema-evolving data like JSON logs, as it automatically reconciles differences between the source data and the catalog definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually update the table schema in the Glue Data Catalog before each run
Why it's wrong here
Manual updates are error-prone and not scalable.
- ✗
Use Spark SQL with a static schema definition in the script
Why it's wrong here
Static schema will fail if the JSON structure changes unexpectedly.
- ✓
Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true
Why this is correct
This allows Glue DynamicFrame to merge schema variations automatically.
- ✗
Enable AWS Glue Schema Registry and define a schema version
Why it's wrong here
Schema Registry enforces schema, but does not automatically handle evolution; manual versioning needed.
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
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