MLA-C01 Data Preparation for Machine Learning Practice Question
A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The source data in S3 has a schema that evolves over time (new columns are added occasionally). The Glue job schema is defined as a fixed schema in the job script. After an update to the source data, the Glue job fails with an error about mismatched schemas. How should the data engineer modify the data preparation process to handle schema evolution?
⚠ Common exam trap
Test-takers frequently assume updating the Data Catalog via a crawler is sufficient, but they miss that the job script's fixed schema must also be updated or made dynamic to avoid mismatches.
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
✓
Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns
AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new columns. By using `applyMapping` with `resolveChoice`, you can define how to handle new fields (e.g., cast to a type or keep as a struct), preventing job failures when the source schema changes. This avoids the rigidity of a fixed schema in the job script.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns
Why this is correct
Dynamic frames with schema detection can adapt to schema changes.
- ✗
Run a Glue crawler before each job to update the Data Catalog, but keep the fixed schema in the job
Why it's wrong here
The job still uses its fixed schema, causing failure.
- ✗
Store the schema definition in a separate file in S3 and read it at runtime
Why it's wrong here
This does not automatically adapt the job to new columns.
- ✗
Manually update the Glue job script each time the schema changes
Why it's wrong here
This is error-prone and not scalable.
Visual reference
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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