DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to run ETL jobs that process data from Amazon S3 and load into Amazon Redshift. The jobs have recently started failing with 'Out of Memory' errors. The data volume has increased 3x in the past month. Which is the MOST effective solution to resolve this issue without redesigning the 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 Glue workers (DPUs) for the job.
To increase the number of Glue workers (DPUs). This provides more memory and processing capacity to handle the increased data volume, directly resolving the 'Out of Memory' errors. Increasing S3 partitions (option D) may improve parallelism but does not directly increase memory for the Glue job. Using Spark SQL (option C) instead of PySpark does not necessarily address memory issues. Switching to Athena (option A) would change the architecture and is not a fix for the existing Glue 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.
- ✗
Use Amazon Athena instead of Glue for the transformation.
Why it's wrong here
Athena is a query service, not an ETL replacement for Glue.
- ✓
Increase the number of Glue workers (DPUs) for the job.
Why this is correct
More workers provide more memory and CPU to handle increased data volume.
- ✗
Rewrite the job to use Spark SQL instead of PySpark.
Why it's wrong here
Spark SQL still runs on the same resources; memory issues remain.
- ✗
Increase the number of partitions in the input S3 data.
Why it's wrong here
More partitions can improve parallelism but do not increase memory per worker.
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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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.