DEA-C01 Data Ingestion and Transformation Practice Question
A company is using AWS Glue to run ETL jobs that read from Amazon S3 and write to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. Which TWO actions should the data engineer take to resolve this issue? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to confuse increasing shuffle partitions (Option D) with a direct fix for OOM errors, when in fact it can increase memory pressure due to more concurrent tasks and metadata overhead, while the correct approach is to allocate more DPUs to scale memory and compute resources.
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
Increasing the number of DPUs allocated to the Glue job (Option B) directly addresses the 'Out of Memory' errors by providing more memory and compute resources per executor. AWS Glue uses Apache Spark under the hood, where each DPU provides 4 vCPU and 16 GB of memory; adding more DPUs increases the total memory available for data processing, reducing the likelihood of OOM errors during shuffle or aggregation operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the output to Amazon S3 instead of Redshift
Why it's wrong here
Does not address the memory issue.
- ✓
Increase the number of DPUs allocated to the Glue job
Why this is correct
More DPUs provide more memory.
- ✗
Reduce the number of partitions in the input data
Why it's wrong here
Reducing partitions may reduce parallelism and increase memory per task, but could also cause OOM if data skew exists.
- ✗
Increase the spark.sql.shuffle.partitions parameter
Why it's wrong here
May help but not the primary fix for OOM.
- ✓
Enable job metrics in CloudWatch to monitor memory usage
Why this is correct
Monitoring helps diagnose the issue.
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 |
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.