DEA-C01 Data Ingestion and Transformation Practice Question
A healthcare company is ingesting patient data from a legacy system into an Amazon S3 data lake using AWS Glue. The legacy system produces CSV files with inconsistent schemas (columns may appear or disappear in different files). The data engineer needs to create a Glue ETL job that can handle schema evolution and transform the data into a standardized parquet format. The job should also be able to process new files as they arrive. Which approach should the data engineer use?
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 AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
AWS Glue DynamicFrames support schema evolution by allowing schema-on-read, and the `resolveChoice` and `applyMapping` transformations can handle inconsistent schemas across CSV files. Option A is wrong because crawlers only catalog schemas, not perform ETL transformations. Option C is wrong because Python shell jobs are not designed for large-scale ETL and lack native schema evolution handling. Option D is wrong because a static schema would reject files with missing or extra columns, failing to handle schema evolution.
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 AWS Glue crawlers to create a schema in the Data Catalog and then use a standard Spark DataFrame for transformation.
Why it's wrong here
Crawlers may not handle schema evolution well.
- ✓
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
Why this is correct
DynamicFrames support schema evolution.
- ✗
Use a Python shell job in Glue to manually parse each file and write to parquet.
Why it's wrong here
Python shell is less efficient and not recommended for ETL.
- ✗
Use a Glue ETL job with a static schema defined in the script and ignore files that don't match.
Why it's wrong here
This would cause data loss.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.