DEA-C01 Data Operations and Support Practice Question
A data engineer maintains an AWS Glue ETL job that processes millions of small JSON files stored in Amazon S3. The job's runtime has increased significantly, and CloudWatch logs show many small executor tasks and frequent garbage collection. The engineer wants to improve job performance by reducing the number of small files processed per task. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming that adding more resources or enabling bookmarks will fix small-file inefficiency, when the real solution is to group files.
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 the Glue ETL job's 'groupFiles' option to group multiple small files into a single partition.
Grouping small files into larger partitions reduces the number of tasks and the overhead of task scheduling and garbage collection. This is a specific optimization for AWS Glue ETL jobs that read many small files. Enabling job bookmarks or increasing DPUs does not address the root cause. Converting formats via a crawler is not a valid approach because crawlers do not transform data.
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 source files to Parquet format using an AWS Glue crawler before running the ETL job.
Why it's wrong here
Converting to Parquet improves storage and query performance for downstream analytics, but the conversion itself would still need to process the many small files, and the ETL job would still read them. The crawler does not convert formats; it only catalogs metadata. This does not solve the small-file processing overhead within the ETL job.
- ✓
Use the Glue ETL job's 'groupFiles' option to group multiple small files into a single partition.
Why this is correct
The 'groupFiles' option in AWS Glue ETL allows grouping multiple small files into a single partition, reducing the number of tasks and improving read efficiency. This directly addresses the issue of many small files causing overhead. By setting groupFiles to 'inPartition' or 'inPartitionAcrossBuckets', the job can process larger chunks of data per task, reducing garbage collection and improving performance.
- ✗
Enable job bookmarks to track previously processed files and skip them on subsequent runs.
Why it's wrong here
Job bookmarks track which files have already been processed in previous runs to avoid reprocessing, but they do not address the current run's inefficiency caused by many small files. The job still reads all new small files in a single run, leading to many small tasks and high garbage collection overhead. Bookmarks help with incremental processing, not with file compaction or task sizing.
- ✗
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 cluster resources, but with many small files, the job still creates numerous small tasks that may not utilize the additional memory efficiently. The overhead of scheduling and garbage collection from small tasks persists. More DPUs can help with large datasets but do not solve the fundamental issue of inefficient file layout.
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.