DEA-C01 Data Operations and Support Practice Question
A data engineer is designing an ETL pipeline that uses AWS Glue to process data from an Amazon DynamoDB table and write results to an S3 bucket in Parquet format. The pipeline must handle schema changes in the source DynamoDB table. Which THREE steps should the engineer take to ensure the pipeline handles schema evolution? (Choose 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 Glue's 'recast' transformation to handle type changes.
Options A, B, and E are correct. Option A: Glue's 'recast' transformation can handle type changes by converting data types as needed. Option B: Setting the Glue crawler to update the table's schema in the Data Catalog ensures that new columns or changes in the source DynamoDB table are reflected. Option E: Using Spark's 'mergeSchema' option when writing to S3 allows Parquet files to have differing schemas, enabling schema evolution. Option C is incorrect because converting Parquet to CSV does not help with schema evolution and may introduce data loss or inefficiency. Option D is incorrect because partitioning by date and deleting old partitions is a data retention strategy, not a schema evolution technique.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Glue's 'recast' transformation to handle type changes.
Why this is correct
recast can change data types to match the target schema.
- ✓
Set the Glue crawler to update the table's schema in the Data Catalog.
Why this is correct
Crawler can update the schema when new columns are detected.
- ✗
Convert the Parquet output to CSV to avoid schema constraints.
Why it's wrong here
CSV does not enforce schema but loses performance benefits of Parquet.
- ✗
Partition the data by date and delete old partitions.
Why it's wrong here
Partition management does not handle schema evolution.
- ✓
Use Spark's 'mergeSchema' option when writing to S3.
Why this is correct
mergeSchema allows Parquet files with different schemas to be written.
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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