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Data Operations and Support →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to increase executor memory and memoryOverhead in the Spark configuration. This directly addresses the PySpark OutOfMemoryError on EMR by allocating more heap and off-heap memory per executor, which is essential when data volume grows by 30% because Spark’s default memory fractions cannot accommodate larger partitions without spilling or crashing. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Spark memory management under the “Tuning and Troubleshooting” domain, where the common trap is to scale horizontally by adding nodes or switching to AWS Glue, both of which introduce cost or code changes without fixing the per-executor memory deficit. The most efficient solution keeps the existing code intact and simply adjusts spark.executor.memory and spark.executor.memoryOverhead in the EMR classification configuration. Memory tip: think of memoryOverhead as the “safety buffer” for PySpark’s Python process and off-heap operations—when data grows, grow the buffer first.

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 data engineer is troubleshooting a nightly ETL job that reads data from an RDS MySQL instance and writes to an S3 bucket in Parquet format. The job runs on an EMR cluster and uses PySpark. Recently, the job started failing with 'OutOfMemoryError' in the executor logs. The data volume has grown 30% in the last month. Which is the MOST efficient solution to resolve this issue without changing the code?

Question 1mediummultiple choice

Full question →

A
Change the RDS instance to a larger size to reduce load.
Why wrong: The OutOfMemoryError occurs in Spark executors, not at the RDS source; increasing RDS size does not address the memory issue in Spark.
B
Switch the ETL job to use AWS Glue with a larger WorkerType.
Why wrong: Migrating to Glue requires code changes and may not resolve the memory issue if the root cause is insufficient Spark configuration.
C
Increase the executor memory and memoryOverhead in the Spark configuration.
Increasing executor memory and memoryOverhead directly addresses the OutOfMemoryError by providing more heap and off-heap memory to executors.
D
Increase the number of core nodes in the EMR cluster.
Why wrong: Adding more nodes increases parallelism but does not increase memory per executor, so the OutOfMemoryError may persist.

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

✓

Increase the executor memory and memoryOverhead in the Spark configuration.

Option A is correct because increasing executor memory and adjusting the spark.executor.memoryOverhead setting addresses memory limitations for large data processing. Option B is wrong because switching to Glue may not directly resolve memory issues and requires code changes. Option C is wrong because increasing cluster size adds cost and may not fix memory per executor. Option D is wrong because using a larger instance type is less flexible than tuning Spark configurations.

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.

✗
Change the RDS instance to a larger size to reduce load.
Why it's wrong here
The OutOfMemoryError occurs in Spark executors, not at the RDS source; increasing RDS size does not address the memory issue in Spark.
✗
Switch the ETL job to use AWS Glue with a larger WorkerType.
Why it's wrong here
Migrating to Glue requires code changes and may not resolve the memory issue if the root cause is insufficient Spark configuration.
✓
Increase the executor memory and memoryOverhead in the Spark configuration.
Why this is correct
Increasing executor memory and memoryOverhead directly addresses the OutOfMemoryError by providing more heap and off-heap memory to executors.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of core nodes in the EMR cluster.
Why it's wrong here
Adding more nodes increases parallelism but does not increase memory per executor, so the OutOfMemoryError may persist.

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.

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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: Increase the executor memory and memoryOverhead in the Spark configuration. — Option A is correct because increasing executor memory and adjusting the spark.executor.memoryOverhead setting addresses memory limitations for large data processing. Option B is wrong because switching to Glue may not directly resolve memory issues and requires code changes. Option C is wrong because increasing cluster size adds cost and may not fix memory per executor. Option D is wrong because using a larger instance type is less flexible than tuning Spark configurations.

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.

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This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.