hardMultiple ChoiceObjective-mapped
DP-203 Practice Question: Refer to the exhibit
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?
⚠ Common exam trap
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.
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 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.
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.
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