Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
A Databricks job fails with an OutOfMemoryError on the driver. The job collects a large DataFrame to the driver for local processing. Which action should you take to resolve this?
⚠ Common exam trap
The trap here is increasing driver memory or maxResultSize instead of reducing the amount of data collected to the driver.
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
✓
Replace collect() with take(n) or write the DataFrame to storage.
The OutOfMemoryError on the driver is caused by collecting a large DataFrame. The correct fix is to avoid transferring all data to the driver by using take() for a sample or writing the results to storage. Increasing driver or executor memory or adjusting shuffle partitions does not address the fundamental problem of excessive data transfer to the driver.
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 executor memory.
Why it's wrong here
Executor memory is used for task execution and caching, not for driver-side operations. The error occurs on the driver because collect() brings all data to the driver. Increasing executor memory will not help and may be wasteful. The driver is a separate JVM with its own memory configuration.
- ✗
Increase spark.driver.maxResultSize.
Why it's wrong here
spark.driver.maxResultSize limits the total size of serialized results that can be sent to the driver. Increasing it might allow a larger collect() to succeed, but it does not solve the root cause of bringing too much data to the driver. It can even lead to worse memory issues or long garbage collection pauses. The better solution is to avoid collecting large datasets.
- ✗
Set spark.sql.shuffle.partitions to a higher value.
Why it's wrong here
Shuffle partitions affect the number of partitions after a shuffle, not the driver memory during collect(). This setting has no direct impact on the OutOfMemoryError caused by collecting a large DataFrame. It might change the parallelism of upstream operations but does not reduce the final data size sent to the driver.
- ✓
Replace collect() with take(n) or write the DataFrame to storage.
Why this is correct
collect() transfers the entire DataFrame to the driver, which can cause an OutOfMemoryError if the data is large. Using take(n) retrieves only a limited number of rows, or writing the DataFrame to storage avoids bringing all data to the driver. This directly addresses the driver memory issue by reducing the amount of data transferred. It is the correct approach.
Visual reference
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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-Spark-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-Spark-Assoc exam.