DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform CSV files arriving in S3 into Parquet format and partition them by date. The transformation should be event-driven and run immediately after each file is uploaded. Which approach is most efficient?
⚠ Common exam trap
Many candidates choose Lambda for its simplicity and event-driven nature, failing to recognize its execution limits and lack of native support for complex transformations like Parquet conversion with partitioning, which Glue is specifically designed to handle.
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 S3 event notification to trigger an AWS Glue job
AWS Glue jobs can be triggered directly by S3 event notifications, enabling event-driven, serverless transformation of CSV to Parquet with partitioning by date. Glue is optimized for this batch ETL workload, handling schema inference and partitioning efficiently without managing infrastructure, making it the most efficient choice for immediate, per-file transformation.
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 S3 event notification to trigger an AWS Glue job
Why this is correct
Glue jobs can be triggered by S3 events and efficiently convert to Parquet with partitioning.
- ✗
Use an S3 event notification to invoke a Lambda function that converts the file
Why it's wrong here
Lambda has timeout limitations and may not be suitable for large CSV files.
- ✗
Use an Amazon EMR cluster running Spark to process files as they arrive
Why it's wrong here
EMR is heavy and costly for this simple transformation.
- ✗
Use Amazon Athena CREATE TABLE AS SELECT (CTAS) on a schedule
Why it's wrong here
CTAS is not event-driven and requires scheduling.
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
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.