Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is analyzing a Spark job that is failing with 'Out of Memory' (OOM) errors. Which configuration parameter should be tuned to increase the amount of memory allocated to the execution of joins and aggregations?
⚠ Common exam trap
Candidates often suggest increasing 'spark.driver.memory' or 'spark.executor.memory'. While these help with total capacity, they do not manage the internal division between storage and execution 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
✓
spark.memory.fraction
Spark's memory management divides heap memory into storage and execution. Joins and aggregations occur in the execution memory. When these operations process large datasets that exceed the allocated execution memory, Spark may fail with OOM. Tuning the memory fraction configuration allows the engineer to shift the balance between storage (for caching) and execution, providing more headroom for complex shuffle-heavy operations like joins and group-by aggregations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
spark.memory.fraction
Why this is correct
This parameter controls the fraction of the heap space used for execution and storage. Increasing this value gives more memory to the execution pool relative to the storage pool, which directly helps in processing large joins and aggregations without running into OOM errors during the shuffle phase.
- ✗
spark.executor.cores
Why it's wrong here
Increasing executor cores allows for more parallel tasks per executor, but it does not expand the total memory available to each task. In fact, if the memory per executor remains constant, increasing cores may lead to even more intense competition for shared memory, exacerbating OOM errors.
- ✗
spark.sql.shuffle.partitions
Why it's wrong here
This parameter controls the number of partitions used in a shuffle. While increasing this value can reduce the amount of data processed per task (potentially helping with memory), it does not directly increase the memory allocated to execution, making it an indirect fix rather than a direct memory configuration.
- ✗
spark.driver.memory
Why it's wrong here
The driver memory parameter controls the memory available to the Spark driver process. Joins and aggregations occur on the worker executors, not the driver. Increasing driver memory will only help if the job fails due to large data collected to the driver, not due to task-level OOM errors.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-DE-Assoc exam.