Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
Exhibit
Error: java.lang.OutOfMemoryError: Java heap space State: FAILED Task: 452 Stage: 12 Action: collect()
Refer to the exhibit. Which action is the most likely cause of the error shown in the Spark job logs?
⚠ Common exam trap
Candidates often assume that calling 'collect()' is a safe way to inspect data, failing to realize it pulls the entire result set into the driver's memory, causing OOM errors.
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
✓
The driver is attempting to aggregate a massive dataset into its memory.
The collect() action attempts to pull the entire DataFrame into the driver node's memory. When the dataset size exceeds the heap space allocated to the driver, a Java heap space error occurs. Developers must avoid collecting large datasets and instead use take() or head() for previews, or write results to cloud storage to maintain application stability when working with big data at scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The executor nodes have insufficient memory for the shuffle operation.
Why it's wrong here
Shuffle OOMs typically manifest as disk space errors or specific shuffle-related task failures, not a general Java heap space error during a collect operation. Since the error is explicitly linked to the collect action, the bottleneck is on the driver's heap, not the executor's shuffle memory buffers.
- ✓
The driver is attempting to aggregate a massive dataset into its memory.
Why this is correct
The collect() method triggers the transfer of all partitions from worker nodes to the driver node. If the combined data exceeds the driver's allocated memory, the JVM throws an OOM error. This is a common architectural mistake when debugging or extracting large-scale distributed data to a single location.
- ✗
The cluster configuration uses too many small partitions.
Why it's wrong here
Small partitions might lead to task scheduling overhead, but they do not cause a Java heap space error on the driver during a collect action. The error is strictly related to the volume of data being moved into memory, not the degree of parallelism defined by the partition count.
- ✗
The broadcast join threshold is set too low for the current job.
Why it's wrong here
Broadcast join issues typically occur during execution stages and would cause OOM errors on the executors, not the driver. Furthermore, these errors usually manifest as broadcast-related exceptions or OOM errors within the executor JVM, rather than the general heap space failure observed during a final collect action.
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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.