DEA-C01 Data Operations and Support Practice Question
A data engineer manages an AWS Glue ETL job that processes millions of small JSON files in Amazon S3. The job is slow and often fails with an OutOfMemory error on the driver. The engineer wants to improve performance without changing the output format. Which solution should the engineer implement?
⚠ Common exam trap
The trap here is assuming that adding more DPUs will always resolve OutOfMemory errors, but driver memory is not proportional to the number of DPUs.
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's groupFiles and groupSize options to combine small files into larger groups.
Grouping small files with groupFiles and groupSize is the most effective way to handle millions of small files in AWS Glue. It reduces the number of input splits, lowers driver memory pressure, and improves overall job performance. Other options either do not address the driver memory issue or require changing the output format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable job bookmarks to track processed files and skip already-processed data.
Why it's wrong here
Job bookmarks track previously processed data to avoid reprocessing, which can reduce runtime for incremental loads. However, in this scenario the job processes millions of small files in a single run, and the OutOfMemory error occurs because too many small files are read simultaneously. Bookmarks do not address the fundamental issue of excessive small files causing driver memory exhaustion, so they will not resolve the failure.
- ✓
Use AWS Glue's groupFiles and groupSize options to combine small files into larger groups.
Why this is correct
The groupFiles and groupSize options in AWS Glue allow the job to coalesce many small files into larger groups before processing. This reduces the number of input partitions and the memory overhead on the driver, directly addressing the OutOfMemory error and improving performance. It is the recommended approach for large numbers of small files without changing the output format.
- ✗
Convert the JSON files to Parquet format using an AWS Glue crawler before running the ETL job.
Why it's wrong here
Converting to Parquet can improve query performance and reduce storage, but the scenario explicitly states the engineer wants to avoid changing the output format. Moreover, the conversion process itself would still need to read the many small JSON files, potentially encountering the same OutOfMemory issue. This approach does not directly address the root cause of the failure.
- ✗
Increase the number of DPUs allocated to the job to provide more memory per executor.
Why it's wrong here
Adding more DPUs increases the total compute capacity, but the driver's memory is fixed per DPU. With millions of small files, the driver still has to manage metadata for each file, and the OutOfMemory error may persist because the driver is the bottleneck. Simply scaling out workers does not solve the problem of excessive small file overhead on the driver.
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.