Databricks-Spark-Assoc Pandas API on Spark Practice Question
Exhibit
Refer to the exhibit. [ERROR LOG] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, last failure: ResultTask(0, 4) failed: java.lang.UnsupportedOperationException: 'to_pandas' is not supported on a DataFrame with a very large size. [END OF EXHIBIT]
Based on the exhibit, what is the most likely reason for this error in a Databricks notebook?
⚠ Common exam trap
Candidates migrating from local Pandas often use 'to_pandas()' indiscriminately on large distributed datasets, forgetting that it gathers all data onto 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
✓
The user is attempting to pull a large distributed dataset into the driver memory.
The 'to_pandas' method collects the entire distributed DataFrame into the driver node's memory. When the DataFrame size exceeds the available memory of the driver, the job fails. This is a common pitfall when transitioning from local Pandas to Pandas-on-Spark, as users might attempt to pull entire datasets into a single machine instead of performing operations within the Spark cluster's distributed environment using the provided API.
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 Spark cluster is configured with insufficient executor memory.
Why it's wrong here
The error occurs at the driver level during the collection process, not the executor level. Executor memory manages the distributed transformation, while the driver is responsible for aggregating the final output of 'to_pandas'. Increasing executor memory will not solve a driver-side memory pressure issue.
- ✗
The DataFrame contains too many columns to be processed.
Why it's wrong here
While many columns increase the memory footprint, the error specifically mentions the DataFrame size being too large for a 'to_pandas' conversion. The limitation is about total volume of data exceeding the driver memory capacity, not the number of columns themselves or a structural limitation.
- ✓
The user is attempting to pull a large distributed dataset into the driver memory.
Why this is correct
The 'to_pandas' operation is a collector that moves data from all Spark executors to the driver. This is intended for small datasets only. When the dataset is too large to fit in the driver's memory, the application will crash, necessitating a different approach like limiting output.
- ✗
The DataFrame has not been properly cached before calling to_pandas.
Why it's wrong here
Caching helps with performance if the data is reused, but it does not change the physical requirement of the driver to hold the entire dataset once 'to_pandas' is invoked. Caching is irrelevant here as it doesn't reduce the final memory footprint required on the driver node.
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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.