DP-700 Monitor and Optimize an Analytics Solution Practice Question
Which configuration would you adjust to improve the performance of a Spark job that is consistently failing due to 'Out of Memory' (OOM) errors during aggregation?
⚠ Common exam trap
Candidates often suggest decreasing the number of partitions to save memory. However, fewer partitions actually increase the data load per partition, which exacerbates Out of Memory errors during heavy aggregation tasks.
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 configuration.
OOM errors during aggregation occur because the data partition is too large to fit in the executor's memory during the shuffle or group-by operation. Increasing the memory per executor or increasing the number of partitions (to make each partition smaller) are standard fixes. By spreading the load, you prevent any single executor from being overwhelmed, ensuring that the aggregation can complete without hitting the rigid memory limits of the compute nodes.
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 driver memory.
Why it's wrong here
Driver memory is only relevant for the orchestrating process that manages tasks. It is not used for data aggregation, which happens in the executors. Increasing driver memory will not help with OOM errors that occur during the actual processing and shuffling of data across the compute executors.
- ✓
Increase the executor memory configuration.
Why this is correct
Aggregation operations are memory-intensive as they require buffering data within the executor. Increasing the executor memory provides more headroom to store intermediate aggregation results, preventing the JVM from running out of memory when processing large datasets or complex grouping operations in the Spark task.
- ✗
Change the table compression to None.
Why it's wrong here
Disabling compression will result in larger data files, which will only increase memory pressure during read and shuffle operations. This would make the OOM error even more likely to occur, as the engine would have to handle significantly more data in memory during the transformation steps.
- ✗
Switch to a different Spark pool version.
Why it's wrong here
Changing the Spark pool version is unlikely to resolve an OOM error caused by a memory-intensive code operation. The error is related to resource allocation and data distribution, not the software version. You must address the memory configuration or the partitioning strategy to solve this specific performance issue.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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