- A
Enable Kryo serialization
Why wrong: Kryo reduces memory usage but may not be sufficient to resolve OOM if memory overhead is too low.
- B
Decrease the number of shuffle partitions
Why wrong: Fewer partitions reduce memory usage but may cause data skew and does not directly address OOM.
- C
Increase the spark.executor.memoryOverhead setting
Memory overhead handles JVM overhead and off-heap memory, preventing OOM errors.
- D
Increase the number of executor cores
Why wrong: More cores can increase parallelism but not memory per executor.
Question 862 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 uses Amazon EMR to run Spark jobs on a large dataset stored in Amazon S3. The jobs are failing with 'OutOfMemoryError' in the executors. The data is not skewed. Which configuration change will most likely resolve the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 spark.executor.memoryOverhead setting
When Spark executors run out of memory during shuffle operations, the `spark.executor.memoryOverhead` setting is often the culprit. This parameter allocates off-heap memory for JVM overhead, internal metadata, and shuffle buffers. Increasing it provides more room for these operations without reducing the executor heap, directly addressing OutOfMemoryError in non-skewed data scenarios.
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.
- ✗
Enable Kryo serialization
Why it's wrong here
Kryo reduces memory usage but may not be sufficient to resolve OOM if memory overhead is too low.
- ✗
Decrease the number of shuffle partitions
Why it's wrong here
Fewer partitions reduce memory usage but may cause data skew and does not directly address OOM.
- ✓
Increase the spark.executor.memoryOverhead setting
Why this is correct
Memory overhead handles JVM overhead and off-heap memory, preventing OOM errors.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of executor cores
Why it's wrong here
More cores can increase parallelism but not memory per executor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse executor memory (heap) with memoryOverhead (off-heap), assuming that increasing heap or reducing partitions will fix all OutOfMemoryErrors, when in fact shuffle-heavy workloads require explicit off-heap tuning.
Detailed technical explanation
How to think about this question
The `spark.executor.memoryOverhead` defaults to 10% of executor memory (minimum 384 MB) and covers off-heap allocations like Netty buffers, thread stacks, and shuffle data. In EMR, YARN container overhead also factors in; increasing this setting ensures the container has enough memory for Spark’s internal operations, especially during large shuffles. A real-world scenario is when executors fail with 'Container killed by YARN for exceeding memory limits'—raising memoryOverhead often fixes it without changing heap size.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the spark.executor.memoryOverhead setting — When Spark executors run out of memory during shuffle operations, the `spark.executor.memoryOverhead` setting is often the culprit. This parameter allocates off-heap memory for JVM overhead, internal metadata, and shuffle buffers. Increasing it provides more room for these operations without reducing the executor heap, directly addressing OutOfMemoryError in non-skewed data scenarios.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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