DEA-C01 Data Ingestion and Transformation Practice Question
A company runs a daily batch ETL job using AWS Glue. The job processes 500 GB of data from Amazon RDS to Amazon S3. The job currently uses a single DPU and takes 6 hours to complete. The team wants to reduce runtime to under 1 hour without increasing costs significantly. Which approach should they use?
⚠ Common exam trap
The trap here is that candidates might think increasing parallelism (Option D) is too expensive, but Glue's pay-per-DPU-second model means a job with 100 workers running for 1 hour costs roughly the same as 1 worker running for 100 hours, so the total cost is similar, not significantly higher.
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 Spark job with 100 workers.
AWS Glue Spark jobs can parallelize data processing across multiple workers, dramatically reducing runtime. With 100 workers, the job can process the 500 GB dataset in parallel, achieving sub-1-hour runtime while keeping costs relatively low since Glue charges per DPU-second and the total DPU-seconds may be similar to the original 6-hour single-DPU job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the job type from Python to Spark.
Why it's wrong here
Glue Python jobs are single-threaded; Spark jobs are parallel but still require enough workers.
- ✗
Use multiple Glue jobs triggered sequentially.
Why it's wrong here
Sequential jobs would not reduce overall runtime.
- ✗
Increase the RDS instance size to improve read throughput.
Why it's wrong here
Database read throughput may not be the bottleneck; Glue parallelism is key.
- ✓
Use AWS Glue Spark job with 100 workers.
Why this is correct
More workers enable parallelism, reducing 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 |
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.