DEA-C01 Data Operations and Support Practice Question
A company stores raw event files in an Amazon S3 bucket that receives thousands of small objects per hour. An AWS Glue job reads the prefix and writes a compacted Parquet dataset to a curated bucket. Operations reports that the Glue job's runtime keeps growing even though the hourly data volume is constant. Which change is MOST likely to reduce runtime?
⚠ Common exam trap
The trap here is assuming runtime scales only with data volume, when per-file overhead from many small objects is usually the dominant cost.
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
✓
Run an S3 compaction or grouping step that merges small objects into larger files before the Glue job reads them.
Glue and Spark incur fixed overhead per input file for listing, opening, and scheduling tasks. Thousands of small objects make that overhead dominate total runtime even when the byte volume is unchanged. Consolidating small files into larger objects before the ETL read reduces the number of files processed and shortens the job, which is the standard remedy for the small-file problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the Glue job's timeout value so long-running reads are not terminated before completion.
Why it's wrong here
Raising the timeout only allows a slow job to keep running longer; it does not make the job faster and can increase cost. If the job is currently completing within its timeout, the parameter is irrelevant. The runtime growth stems from per-object overhead, which a timeout change cannot influence.
- ✗
Enable S3 Transfer Acceleration on the raw bucket so Glue can download objects faster.
Why it's wrong here
Transfer Acceleration speeds up uploads and downloads over long geographic distances by routing through edge locations. Glue jobs run inside AWS Regions, where acceleration provides little benefit and adds cost. The bottleneck described is the number of small objects rather than transfer distance, so acceleration would not meaningfully shorten the run.
- ✗
Add a Glue job bookmark to the raw prefix and schedule the compaction job to run hourly instead of daily.
Why it's wrong here
A bookmark would prevent reprocessing old files, which helps if the job is rescanning history, but the scenario states volume per hour is constant and the job already reads the prefix. More frequent scheduling increases total runs rather than reducing per-run work. The core problem of many small input files remains unaddressed.
- ✓
Run an S3 compaction or grouping step that merges small objects into larger files before the Glue job reads them.
Why this is correct
Each small object incurs per-file overhead for listing, opening, and task scheduling, so thousands of tiny files dominate runtime regardless of total bytes. Merging them into larger objects, for example with an S3 Batch Operations copy or a preceding compaction job, cuts the number of files Glue must open and lets Spark read efficiently, which directly reduces runtime.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.