DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The jobs run nightly and take 3 hours to complete. The data volume is growing by 20% each month. The engineer needs to reduce job runtime and cost. The source RDS is a db.r5.large instance. Which approach would be MOST effective?
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 Glue workers and choose a G.1X or G.2X worker type.
Increasing the number of Glue workers and choosing a G.1X or G.2X worker type directly increases parallelism and provides more memory per worker, reducing job runtime at a manageable cost increase. Option A is wrong; reducing DPUs would increase runtime, not reduce it. Option C is wrong because a read replica does not improve Glue processing speed; the bottleneck is Glue's processing capacity, not the source database's read capacity. Option D is wrong because S3 Transfer Acceleration improves upload speed to S3, not Glue job processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of DPUs to lower cost and accept longer runtime.
Why it's wrong here
Fewer DPUs reduces cost but lengthens runtime, directly contradicting the requirement to cut both, and the 20% monthly growth worsens it. It tempts because right-sizing worker count is a legitimate cost lever; it would be correct if the job were over-provisioned and runtime headroom existed.
- ✓
Increase the number of Glue workers and choose a G.1X or G.2X worker type.
Why this is correct
Glue scales horizontally, so adding workers and moving to G.1X or G.2X types increases per-worker compute and memory, cutting the three-hour runtime. This addresses the growing data volume without the cost of resizing the source RDS instance, which is not the bottleneck.
- ✗
Create a read replica of the RDS instance and point the Glue job to the replica.
Why it's wrong here
A read replica offloads query traffic from the primary but Glue still reads the same volume of rows, so extraction time and DPU cost are unchanged. Replicas are tempting because they genuinely relieve contention on production databases; they would be correct if the primary's load, not Glue runtime, were the bottleneck.
- ✗
Enable S3 Transfer Acceleration on the destination bucket.
Why it's wrong here
Transfer Acceleration speeds uploads to S3 from distant clients, yet the bottleneck is reading from RDS and Glue transformation, leaving runtime and cost untouched. It tempts because it addresses data movement; it would be correct for geographically dispersed clients uploading large objects over long-haul links.
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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JA
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.