DEA-C01 Data Store Management Practice Question
A data engineering team is using AWS Glue to catalog data in an S3 data lake. They have a Glue crawler that runs daily to update the Data Catalog. Recently, they noticed that the crawler is taking longer to run and sometimes fails because of a timeout. The team suspects the issue is due to the large number of small files in the S3 bucket. They need to improve crawler performance and reliability. Which solution should they implement?
⚠ Common exam trap
A common mix-up: candidates assume increasing the timeout or running the crawler more frequently will fix performance issues, but the real bottleneck is the sheer number of small files, which requires data compaction to resolve.
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 ETL to consolidate small files into larger ones before crawling.
Consolidating small files into larger ones (e.g., using AWS Glue ETL with a groupFiles or groupSize option, or a separate compaction job) reduces the number of objects the crawler must list and sample. This directly addresses the root cause: a high volume of small files increases metadata operations and can cause crawler timeouts. By reducing file count, the crawler can complete within the default 24-hour timeout and avoid failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the crawler to use a different classifier.
Why it's wrong here
A different classifier changes how the crawler parses file contents and infers schemas; it does nothing about the file enumeration overhead causing the timeout. It is tempting because classifiers genuinely fix misparsed or unrecognised formats, which is the right remedy when schema inference, not file count, is failing.
- ✓
Use AWS Glue ETL to consolidate small files into larger ones before crawling.
Why this is correct
Consolidating many small S3 objects into fewer larger files via Glue ETL reduces the metadata and listing overhead the crawler processes each run. This directly addresses the small-file volume causing slow runs and timeouts, restoring crawler performance and reliability.
- ✗
Increase the crawler timeout to 24 hours.
Why it's wrong here
Raising the timeout to 24 hours leaves the underlying cause untouched: the crawler still enumerates every small file individually, so runtime and failure risk persist. It is tempting because timeouts are the visible symptom, and longer timeouts genuinely help crawlers processing legitimately large but well-partitioned datasets.
- ✗
Schedule the crawler to run more frequently to avoid large data accumulation.
Why it's wrong here
Running the crawler more frequently compounds the problem, since each run still enumerates the same proliferation of small files and adds scheduling overhead. It is tempting because frequent runs suit slowly changing datasets where each crawl is cheap, but here file count, not data volume, drives the timeout.
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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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.