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Question 1,734 of 1,786

Data Operations and Support →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to change the core node instance type to r5.xlarge and keep 5 nodes, as this directly addresses Spark out-of-memory errors in EMR by doubling per-node memory from 16 GB to 32 GB without increasing cluster size. When processing 500 GB of Parquet data with default Spark configurations, the m5.xlarge instances lack sufficient executor memory for shuffle operations and data caching, causing the OutOfMemoryError. This scenario tests your understanding of instance family trade-offs on the AWS Certified Data Engineer Associate DEA-C01 exam, where a common trap is to add more nodes (which increases cost linearly) or to blindly increase spark.executor.memory beyond physical limits (which YARN will reject). Memory-optimized instances like the r5 family provide the most cost-effective resolution because they boost memory per vCPU without incurring additional node overhead. Remember the mnemonic: "R for RAM" — when Spark runs out of room, switch to the R5 family.

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 data pipeline that uses Amazon EMR to process large datasets. The pipeline reads data from S3, processes it using Spark, and writes results back to S3. Recently, the pipeline has been failing with 'OutOfMemoryError' in the Spark executors. The EMR cluster is configured with 5 core nodes of type m5.xlarge (4 vCPU, 16 GB memory each). The Spark application uses dynamic allocation and default Spark configurations. The input data size is approximately 500 GB in Parquet format. What is the most cost-effective way to resolve the out-of-memory errors?

Question 1hardmultiple choice

Full question →

A
Increase the spark.executor.memory setting to 8 GB in the Spark configuration.
Why wrong: The default executor memory is already around 4 GB; increasing to 8 GB may exceed container limit and cause YARN to kill executors.
B
Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes.
Memory-optimized instances provide more memory per node, reducing OOM without increasing node count.
C
Increase the number of core nodes to 10 to distribute the data across more executors.
Why wrong: Adding nodes increases cost and may not solve OOM if each executor still has limited memory.
D
Change the input data format from Parquet to ORC to reduce memory footprint.
Why wrong: Both formats are columnar and similar in memory usage; format change is unlikely to fix OOM.

Full breakdown with real-world context →

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

✓

Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes.

Option B is correct because the current cluster has limited memory per node (16 GB). By switching to memory-optimized instances like r5.xlarge (32 GB), each node has double the memory, reducing the chance of OOM. This is more cost-effective than adding more nodes because the total memory per node increases without increasing the number of instances. Option A is wrong because increasing the number of nodes adds more memory but also more cost; it might be more expensive than using fewer, larger nodes. Option C is wrong because it's generally not recommended to increase spark.executor.memory beyond the physical memory; it could cause YARN to kill containers. Option D is wrong because Parquet is already efficient; changing to a different format may not solve memory issues.

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 spark.executor.memory setting to 8 GB in the Spark configuration.
Why it's wrong here
The default executor memory is already around 4 GB; increasing to 8 GB may exceed container limit and cause YARN to kill executors.
✓
Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes.
Why this is correct
Memory-optimized instances provide more memory per node, reducing OOM without increasing node count.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of core nodes to 10 to distribute the data across more executors.
Why it's wrong here
Adding nodes increases cost and may not solve OOM if each executor still has limited memory.
✗
Change the input data format from Parquet to ORC to reduce memory footprint.
Why it's wrong here
Both formats are columnar and similar in memory usage; format change is unlikely to fix OOM.

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.

Trap categories for this question

Similar concept trap
Both formats are columnar and similar in memory usage; format change is unlikely to fix OOM.

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

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: Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes. — Option B is correct because the current cluster has limited memory per node (16 GB). By switching to memory-optimized instances like r5.xlarge (32 GB), each node has double the memory, reducing the chance of OOM. This is more cost-effective than adding more nodes because the total memory per node increases without increasing the number of instances. Option A is wrong because increasing the number of nodes adds more memory but also more cost; it might be more expensive than using fewer, larger nodes. Option C is wrong because it's generally not recommended to increase spark.executor.memory beyond the physical memory; it could cause YARN to kill containers. Option D is wrong because Parquet is already efficient; changing to a different format may not solve memory issues.

What should I do if I get this DEA-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

1 more ways this is tested on DEA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company is using Amazon EMR to run Spark jobs. The jobs are failing due to memory issues. Which THREE configurations can help mitigate out-of-memory errors?

hard

A.Configure instance store volumes for intermediate shuffle data.
B.Use instances with more vCPUs to process more tasks in parallel.
✓ C.Tune Spark memory configurations like spark.executor.memory and spark.memory.fraction.
✓ D.Increase the instance type to one with more memory per node.
✓ E.Enable Spark dynamic allocation to adjust executors based on workload.

Why C: Options A, B, and D are correct. Increasing instance memory, tuning memory fractions, and enabling dynamic allocation help manage memory. Option C is wrong because more cores per node increases parallelism, potentially worsening memory pressure. Option E is wrong because instance store volumes are for temporary storage, not memory.

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