DEA-C01 Data Operations and Support Practice Question
A data engineer is troubleshooting an Amazon EMR cluster that has been running for several days. The cluster uses Amazon S3 as the data source and HDFS for intermediate storage. The engineer notices that some tasks fail with 'Java heap space' errors. Which TWO actions should the engineer take to resolve this issue?
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 maximum Java heap size for the task nodes (mapreduce.map.java.opts).
Options C and D are correct. Increasing the maximum Java heap size (mapreduce.map.java.opts) gives each task more memory to avoid heap space errors. Increasing YARN memory overhead (yarn.nodemanager.resource.memory-mb) allows containers to request more memory from YARN. Option A is incorrect because EMRFS consistent view addresses S3 consistency, not memory. Option B is incorrect because increasing the number of containers per node without increasing total memory reduces memory per container, worsening heap issues. Option E is incorrect because decreasing YARN container size reduces available memory, counterproductive for out-of-memory errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable EMRFS consistent view for S3.
Why it's wrong here
EMRFS consistent view addresses eventual consistency issues with S3, not memory constraints. It will not resolve Java heap space errors.
- ✗
Increase the number of containers per node.
Why it's wrong here
Increasing the number of containers per node without increasing the total memory on the node will reduce the memory available to each container, potentially making Java heap space problems worse.
- ✓
Increase the maximum Java heap size for the task nodes (mapreduce.map.java.opts).
Why this is correct
Increasing the maximum Java heap size for task nodes (mapreduce.map.java.opts) gives each task more memory, directly addressing the 'Java heap space' errors.
- ✓
Increase the YARN memory overhead parameter (yarn.nodemanager.resource.memory-mb).
Why this is correct
Increasing the YARN memory overhead parameter (yarn.nodemanager.resource.memory-mb) allows containers to request more memory from YARN, helping to avoid out-of-memory errors.
- ✗
Decrease the YARN container size.
Why it's wrong here
Decreasing the YARN container size reduces the memory available to each container, which would exacerbate Java heap space issues.
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.