DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue ETL job that reads from an Amazon S3 bucket with millions of small JSON files. The job is running slowly and often fails with out-of-memory errors. The engineer needs to improve performance and reliability. What should the engineer do?
⚠ Common exam trap
The trap here is assuming that simply adding more DPUs will solve performance problems caused by many small files, when the real fix is to reduce file count through grouping.
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 parameters to combine small files into larger groups.
When an AWS Glue job reads millions of small files, the overhead of listing and opening each file causes performance degradation and memory issues. The groupFiles and groupSize parameters allow Glue to combine multiple small files into larger read groups, reducing task count and improving throughput. This is the most direct and effective solution for this scenario.
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 process only new files and reduce the workload.
Why it's wrong here
Job bookmarks track previously processed data to avoid reprocessing, but they do not help with the initial backlog of millions of small files. The performance issue is due to the file layout, not reprocessing. Bookmarks are useful for incremental loads but do not address the out-of-memory errors caused by many small files.
- ✓
Use AWS Glue's groupFiles and groupSize parameters to combine small files into larger groups.
Why this is correct
AWS Glue provides groupFiles and groupSize to coalesce multiple small files into a single partition read. This reduces the number of tasks and metadata overhead, improving performance and reducing memory pressure. It is the recommended approach for large numbers of small files in S3 when using Glue ETL.
- ✗
Increase the number of DPUs for the Glue job to handle the large number of files.
Why it's wrong here
Adding more DPUs increases compute and memory, but the core issue is the millions of small files causing excessive metadata operations and task overhead. More DPUs may temporarily help but do not address the inefficient file layout. The job may still fail due to too many partitions and small file reads.
- ✗
Convert the JSON files to Parquet using an AWS Glue crawler before running the ETL job.
Why it's wrong here
A Glue crawler catalogs metadata; it does not convert file formats. Converting to Parquet would require an ETL job or another tool. While Parquet is more efficient, the immediate problem is the small file count, not the format. This option misstates the crawler's capability and does not solve the performance issue.
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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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.