Fix Spark OOM Errors by Increasing Executor Memory in Azure Synapse
This DP-203 practice question tests your understanding of design and develop data processing. 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.
Network Topology
Refer to the exhibit. A data engineer runs a Synapse Spark job that fails with the error shown. Which configuration change is most likely to 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.
Network Topology
A
Change the executor cores to 1
Why wrong: Changing cores does not directly increase memory; the OOM is due to insufficient heap space per executor.
B
Switch to a different Spark pool with the same configuration
Why wrong: Switching pools does not change memory allocation; the same configuration would likely produce the same error.
C
Reduce the number of executors to 1 for Job1
Why wrong: Reducing executors would decrease parallelism but not increase heap space per executor; it may worsen memory issues.
D
Increase executor memory to 4g for Job1
Increasing executor memory provides more heap space, which directly addresses the OutOfMemoryError.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase executor memory to 4g for Job1
The error indicates an out-of-memory (OOM) condition in the Spark executor. Increasing executor memory to 4g for Job1 provides more heap space for data processing, which directly resolves the memory exhaustion. This is the most appropriate fix because the error is specifically about memory, not CPU or parallelism.
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 executor cores to 1
Why it's wrong here
Changing cores does not directly increase memory; the OOM is due to insufficient heap space per executor.
✗
Switch to a different Spark pool with the same configuration
Why it's wrong here
Switching pools does not change memory allocation; the same configuration would likely produce the same error.
✗
Reduce the number of executors to 1 for Job1
Why it's wrong here
Reducing executors would decrease parallelism but not increase heap space per executor; it may worsen memory issues.
✓
Increase executor memory to 4g for Job1
Why this is correct
Increasing executor memory provides more heap space, which directly addresses the OutOfMemoryError.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might confuse memory issues with parallelism or executor count, leading them to choose options that reduce parallelism (A or C) instead of directly increasing memory.
Detailed technical explanation
How to think about this question
In Apache Spark, executor memory is allocated via `spark.executor.memory` and is used for both storage and shuffle operations. When an executor runs out of memory, it throws a `java.lang.OutOfMemoryError`, often during shuffle or when caching large DataFrames. Increasing executor memory allows more data to be held in memory, reducing spill to disk and preventing OOM. In Synapse Spark pools, this setting can be adjusted per job via Spark configuration or the pool's default settings.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Visual reference
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and develop data processing — This question tests Design and develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase executor memory to 4g for Job1 — The error indicates an out-of-memory (OOM) condition in the Spark executor. Increasing executor memory to 4g for Job1 provides more heap space for data processing, which directly resolves the memory exhaustion. This is the most appropriate fix because the error is specifically about memory, not CPU or parallelism.
What should I do if I get this DP-203 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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