DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The source files are in a bucket with thousands of small files. What is the best practice to optimize the Glue job performance?
⚠ Common exam trap
A common mix-up: candidates assume more DPUs always improve performance, but for small files the bottleneck is metadata overhead, not compute capacity, so increasing DPUs without addressing file grouping leads to wasted resources and no speedup.
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
✓
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Enabling 'Group small files' in AWS Glue automatically coalesces thousands of small input files into larger partitions, reducing the number of tasks and minimizing overhead from task scheduling and S3 list operations. This is the recommended best practice for handling small files in Glue ETL jobs, as it optimizes read performance without requiring manual coalesce or repartitioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the JSON files to CSV before processing with Glue.
Why it's wrong here
CSV is less efficient; Parquet is better.
- ✓
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Why this is correct
Grouping reduces the number of tasks and improves performance.
- ✗
Use an AWS Lambda function to pre-process the files.
Why it's wrong here
Lambda adds complexity and cost.
- ✗
Increase the number of DPUs to the maximum.
Why it's wrong here
Small files cause overhead regardless of DPUs.
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.