DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to transform data from Amazon RDS to Amazon S3 daily. The job recently started failing with memory errors. The data volume has grown 3x in the past month. Which change should the data engineer make to resolve the issue?
⚠ Common exam trap
Candidates often confuse scaling the source database (RDS) with scaling the ETL compute (Glue), or assume that output partitioning (S3) will fix an in-memory processing error, when the actual solution is to increase the compute resources allocated to the Glue job.
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 DPUs allocated to the Glue job
The Glue job is failing with memory errors due to a 3x increase in data volume. Increasing the number of DPUs (Data Processing Units) allocated to the job provides more memory and compute resources, directly addressing the out-of-memory condition without changing the job logic or architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the size of the Amazon RDS instance
Why it's wrong here
Scaling Amazon RDS addresses the source database, yet the memory errors occur inside the Glue ETL job's executors, which run independently of RDS instance size. A larger RDS instance would be right for source-side performance or connection limits, not Glue memory exhaustion.
- ✗
Switch the Glue job type from Python Shell to Spark
Why it's wrong here
Python Shell jobs run single-node with a fixed memory ceiling and cannot scale out, so switching to Spark does not itself add capacity unless worker count and type are also raised. Python Shell suits small, lightweight scripts, not 3x-growth datasets.
- ✗
Partition the output data in Amazon S3 by date
Why it's wrong here
Partitioning output in Amazon S3 improves downstream read performance and reduces scan costs, but it does not reduce the memory consumed during the Glue transformation itself. It would be correct for query efficiency, not for resolving executor out-of-memory failures.
- ✓
Increase the number of DPUs allocated to the Glue job
Why this is correct
Glue allocates memory per DPU, so tripling data volume exhausts the current worker capacity. Increasing DPUs scales the compute and memory available to each task, resolving the out-of-memory failures. This directly addresses the resource constraint created by the threefold data growth.
Visual reference
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.