DEA-C01 Data Operations and Support Practice Question
A company runs a batch ETL job on Amazon EMR every night. Recently, the job started failing with 'Out of Memory' errors in the Spark executors. The data volume has grown 20% in the past month. The cluster uses uniform instance groups with 5 core nodes of r5.xlarge (4 vCPU, 32 GB RAM). Which change should the data engineer implement to resolve the issue with minimal cost increase?
⚠ Common exam trap
Many candidates assume increasing the number of nodes (Option A) or tuning Spark memory settings (Option D) can solve memory issues, but they fail to recognize that the root cause is insufficient memory per executor, which is best addressed by adding larger instances via instance fleets to minimize cost increase.
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
✓
Configure instance fleets to include r5.xlarge and r5.2xlarge instances.
Using instance fleets allows the cluster to include both r5.xlarge and r5.2xlarge instances, enabling the Spark executors to use the larger instances for memory-intensive tasks while still leveraging the existing r5.xlarge nodes. This provides a cost-effective way to handle the 20% data growth by adding memory capacity without replacing the entire cluster or over-provisioning all nodes. Instance fleets also support Spot Instances, which can further reduce costs while addressing the 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.
- ✗
Increase the number of core nodes to 7.
Why it's wrong here
Adding nodes increases cost and may not target the memory issue efficiently.
- ✗
Change instance type to r5.2xlarge (8 vCPU, 64 GB RAM) for all nodes.
Why it's wrong here
Doubling memory and vCPU is more expensive than a mixed fleet.
- ✓
Configure instance fleets to include r5.xlarge and r5.2xlarge instances.
Why this is correct
Instance fleets allow cost-effective scaling by mixing types.
- ✗
Tune Spark memory configurations to reduce executor memory overhead.
Why it's wrong here
Tuning may help but likely insufficient for a 20% data growth.
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