The answer is to increase the number of reducers (mapreduce.reduce.tasks) while keeping the same m5.xlarge instance type. This is correct because an OutOfMemoryError in the reduce phase indicates that each reducer is being overwhelmed by the volume of data it must process; by increasing the number of reducers, you distribute the memory load across more parallel tasks, allowing each reducer to handle a smaller partition of data without exceeding its heap limit. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of tuning MapReduce memory allocation versus scaling hardware—a common trap is to immediately choose memory-optimized instances like r5, which are more expensive and unnecessary when the root cause is task parallelism, not insufficient RAM per node. Remember the mnemonic: “More reducers, less pressure—memory errors surrender.”
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 nightly Amazon EMR job that processes data from S3 and writes results back to S3. The job fails with 'OutOfMemoryError' in the reduce phase. The cluster currently uses 5 m5.xlarge instances. Which cost-effective change should the data engineer make?
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 reducers (mapreduce.reduce.tasks) and keep the same instance type.
Option A is correct because increasing the number of reducers distributes the memory load, and m5.xlarge instances are cost-effective. Option B is wrong because r5 instances are memory-optimized but more expensive. Option C is wrong because increasing instance count may not help if reducer memory is the issue. Option D is wrong because reducing input size may affect completeness.
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 10.
Why it's wrong here
More nodes do not directly reduce reducer memory pressure.
✓
Increase the number of reducers (mapreduce.reduce.tasks) and keep the same instance type.
Why this is correct
More reducers reduce memory per reducer, preventing OOM.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the input data size by filtering early in the job.
Why it's wrong here
Filtering may skip important data; not a reliable fix.
✗
Switch to r5.xlarge instances for more memory per instance.
Why it's wrong here
r5 instances are costlier; increasing reducers is cheaper.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Increase the number of reducers (mapreduce.reduce.tasks) and keep the same instance type. — Option A is correct because increasing the number of reducers distributes the memory load, and m5.xlarge instances are cost-effective. Option B is wrong because r5 instances are memory-optimized but more expensive. Option C is wrong because increasing instance count may not help if reducer memory is the issue. Option D is wrong because reducing input size may affect completeness.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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