Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
Exhibit
Error: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 1000 tasks (2048 MB) is bigger than spark.driver.maxResultSize (1024 MB)
Refer to the exhibit. A data engineer receives this error when collecting data from a large transformation back to the driver node. Which approach should be used to fix this issue?
⚠ Common exam trap
Candidates frequently suggest increasing the driver memory or the Spark memory configuration, which is a temporary fix that ignores the fundamental architectural flaw of pulling large datasets into the driver node.
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
✓
Rewrite the job to write the results to a Delta table.
The error occurs because the result set being pulled to the driver exceeds the configured limit. Collecting massive data to the driver is an anti-pattern in distributed computing as it bypasses the cluster's parallel processing capabilities. Instead of forcing data into the driver's memory, the engineer should write the output to cloud storage or a Delta table, allowing downstream processes to handle the data in a distributed, scalable manner without memory pressure.
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 spark.driver.maxResultSize setting.
Why it's wrong here
While increasing this configuration might temporarily mask the error, it does not fix the underlying architectural flaw of collecting massive data on a single node. This approach risks crashing the driver node with an OutOfMemoryError, as the driver is not designed for heavy data processing.
- ✓
Rewrite the job to write the results to a Delta table.
Why this is correct
Writing the results to a Delta table ensures the data is persisted in a distributed format on storage. This avoids overwhelming the driver node and allows subsequent tasks to consume the data in parallel, which is the standard, scalable pattern for handling large datasets in Databricks.
- ✗
Reduce the number of tasks in the Spark job.
Why it's wrong here
Reducing the number of tasks does not resolve the total size of the result set being sent to the driver. The issue is the volume of data collected, not the number of tasks. This change might actually decrease performance by underutilizing the available cluster executors.
- ✗
Enable Spark dynamic allocation.
Why it's wrong here
Dynamic allocation manages the number of executors in the cluster based on workload. It does not control the memory limits of the driver node or the result size constraints imposed by Spark when collecting data, making it ineffective for resolving the specific error regarding maxResultSize.
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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.