DEA-C01 Data Operations and Support Practice Question
A data engineer is running an Amazon EMR cluster with Spark to process log files. The cluster uses instance fleets with m5.xlarge core nodes. The engineer observes that the Spark job is running slower than expected. CloudWatch metrics show that the cluster's CPU utilization is below 20% but memory utilization is near 90%. Which configuration change would most likely improve performance?
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
✓
Use memory-optimized instances (r5.xlarge) for core nodes.
High memory utilization (90%) with low CPU (<20%) indicates that the data does not fit in memory, causing frequent spills to disk. Using memory-optimized instances (r5.xlarge) provides more memory per vCPU compared to m5.xlarge, allowing more data to be kept in memory and reducing spills. Option B is incorrect because increasing the number of core nodes adds more CPU and memory overall, but each node still has the same memory-to-CPU ratio (8 GB per 4 vCPUs for m5.xlarge), so memory pressure per node remains. Option C is incorrect because the issue is insufficient memory, not the number of shuffle partitions; adjusting shuffle partitions does not increase available memory. Option D is incorrect because decreasing the number of core nodes reduces total cluster memory, worsening the memory bottleneck.
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 memory-optimized instances (r5.xlarge) for core nodes.
Why this is correct
r5 instances have higher memory-to-CPU ratio, reducing memory pressure and spills.
- ✗
Increase the number of core nodes from 5 to 10.
Why it's wrong here
Adding more nodes adds CPU and memory, but the memory per node remains the same, and the job may still spill.
- ✗
Increase the number of Spark shuffle partitions.
Why it's wrong here
Shuffle partitions affect parallelism but not memory usage per task.
- ✗
Decrease the number of core nodes to reduce overhead.
Why it's wrong here
Reducing nodes reduces total memory, worsening the problem.
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