Databricks-DE-Pro Debugging and Deploying Practice Question
You are monitoring a long-running Databricks job. You notice that the memory usage on the driver node is steadily increasing until it crashes. Which debugging action is most appropriate?
⚠ Common exam trap
Candidates often suggest increasing the driver instance size as the primary solution. This ignores the root cause, which is an architectural flaw in the code pulling data into 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
✓
Use the Spark UI to identify tasks using 'collect()' or 'toPandas()' on large datasets.
Driver memory crashes are typically caused by collecting large datasets from workers to the driver. By identifying the 'collect' or 'toPandas' operations, you can optimize the code to process data in a distributed manner. This is critical for scaling data pipelines; understanding why the driver is overloaded ensures that jobs remain performant as data volumes grow and prevents common failures in production batch processing environments.
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 number of worker nodes in the cluster.
Why it's wrong here
Increasing worker count does not alleviate memory pressure on the driver node. In fact, if the driver is collecting data from a larger number of workers, the memory bottleneck might worsen. Driver-side issues must be addressed by changing how data is gathered or processed, not by adding workers.
- ✓
Use the Spark UI to identify tasks using 'collect()' or 'toPandas()' on large datasets.
Why this is correct
The Spark UI allows you to inspect the execution plan and identify operations that move data from worker nodes to the driver node. Using 'collect()' on large datasets is a classic cause of driver out-of-memory errors. Identifying these bottlenecks allows you to replace them with distributed data writing.
- ✗
Update the cluster's Spark configuration to disable the driver's log monitoring.
Why it's wrong here
Disabling log monitoring does not resolve memory leaks or inefficient code patterns. While it might marginally reduce the memory footprint, it is a dangerous practice that obscures root causes and prevents the engineer from monitoring the health of the application. It does not address the underlying architectural problem.
- ✗
Lower the 'spark.driver.maxResultSize' setting in the cluster configuration.
Why it's wrong here
Lowering the maxResultSize will only cause the job to fail faster with a 'Result size exceeded' error. It does not solve the underlying issue of why the driver is being asked to handle more data than it can manage. This setting is a safeguard, not a solution for performance.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-Pro 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-Pro exam.