DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue DataBrew for data preparation. The data source is an S3 bucket with millions of small CSV files (each < 1 MB). The DataBrew project takes a long time to load the sample data. What is the most likely cause and solution?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that increasing DPUs or switching to a different AWS service will fix performance issues, when the real root cause is S3's small-file overhead and the LIST API's pagination limit.
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
✓
The large number of small files causes S3 LIST overhead; concatenate files into larger files
DataBrew loads a sample of the data by listing objects in the S3 bucket. With millions of small CSV files, the S3 LIST API call becomes a bottleneck because each list operation has a 1000-object limit per response, requiring multiple paginated requests. Concatenating the small files into larger files reduces the number of objects, dramatically decreasing LIST overhead and speeding up sample loading.
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 Amazon Athena to query the data instead of DataBrew
Why it's wrong here
Athena also performs poorly with many small files due to high overhead.
- ✗
The DataBrew job is under-provisioned; increase the number of DPUs
Why it's wrong here
DPUs help with processing, but the bottleneck is reading many small files from S3.
- ✓
The large number of small files causes S3 LIST overhead; concatenate files into larger files
Why this is correct
S3 performance degrades with many small files; combining them reduces API calls.
- ✗
Use AWS Glue ETL instead of DataBrew for this volume
Why it's wrong here
Glue ETL also faces similar S3 overhead issues with many small files.
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
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,711-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.