The answer is to configure instance fleets to include both r5.xlarge and r5.2xlarge instances. This resolves the EMR Spark out of memory error by allowing the cluster to dynamically allocate larger executor containers on the r5.2xlarge nodes for memory-intensive tasks, while still using the existing r5.xlarge nodes for lighter workloads, providing a cost-effective solution that avoids replacing the entire cluster. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how instance fleets offer flexible capacity and support mixed instance types, including Spot Instances, to handle variable data growth without over-provisioning. A common trap is assuming you must scale uniformly with larger instance groups, which increases costs unnecessarily. Memory tip: think “fleet for flexibility” — instance fleets let you mix sizes to match memory demand, not just add more of the same.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Instance fleets in Amazon EMR allow you to specify multiple instance types and purchasing options (On-Demand and Spot) within a single fleet, enabling the cluster to automatically launch the most cost-effective instances that meet your capacity requirements. When Spark executors run on a mix of instance types, the memory per executor is determined by the instance type it runs on, so using larger instances (r5.2xlarge) for memory-intensive tasks can prevent Out of Memory errors without requiring uniform instance types. Under the hood, Spark's dynamic allocation can adjust the number of executors based on workload, but it cannot overcome physical memory limits of individual nodes; instance fleets provide the flexibility to add memory capacity where needed.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure instance fleets to include r5.xlarge and r5.2xlarge instances. — Option C is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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