DEA-C01 Data Operations and Support Practice Question
A data engineer is running a Glue ETL job that reads from a JDBC source and writes to S3 in Parquet format. The job is slow and the engineer notices that the number of DPUs used is low. What can be done to improve performance?
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
✓
Increase the number of workers (MaxCapacity) in the job configuration.
Increasing the number of workers (MaxCapacity) directly increases the parallelism of the Glue ETL job. Since the DPU usage is low, the job is not fully utilizing available resources; adding workers allows more concurrent processing, thus improving performance. Option A: Disabling job bookmarks avoids reading metadata but does not address low DPU usage and may cause reprocessing. Option B: Push-down predicates filter data at the source, reducing data volume, but the primary bottleneck here is parallelism, not data volume. Option D: Changing output to CSV would increase overhead and worsen performance because Parquet is columnar and compressed, while CSV is not.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable job bookmarks to avoid reading metadata.
Why it's wrong here
Disabling job bookmarks avoids reading metadata but does not directly improve parallelism or DPU utilization; it may even cause reprocessing of data, which could hurt performance.
- ✗
Use push-down predicates to filter data at the source.
Why it's wrong here
Push-down predicates filter data at the source, reducing the amount of data read. This can improve performance in some cases, but the low DPU usage indicates the bottleneck is parallelism, not data volume.
- ✓
Increase the number of workers (MaxCapacity) in the job configuration.
Why this is correct
Increasing the number of workers (MaxCapacity) increases the number of parallel executors, directly addressing the low DPU usage and improving job performance.
- ✗
Change the output format to CSV to reduce CPU overhead.
Why it's wrong here
Changing the output format to CSV would increase storage size and CPU overhead for compression, degrading performance compared to Parquet.
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 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.