Databricks-Spark-Assoc Pandas API on Spark Practice Question
You are analyzing a large dataset using Pandas API on Spark. You have a Pandas-on-Spark DataFrame `psdf` that was created from a Spark DataFrame with multiple partitions. You call `psdf.head(10)` to quickly inspect the data. What does this operation return?
⚠ Common exam trap
The trap here is assuming that `head` returns a distributed DataFrame, but it actually returns a local pandas DataFrame for immediate inspection.
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
✓
A standard pandas DataFrame containing the first 10 rows.
The `head(n)` method in Pandas API on Spark returns a local pandas DataFrame containing the first n rows. It is intended for quick inspection of data, and it collects only the necessary rows to the driver. This behavior matches the pandas API, where `head` returns a DataFrame, but here it triggers a small action to bring data locally.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Pandas-on-Spark DataFrame containing the first 10 rows.
Why it's wrong here
This is incorrect because `head(10)` on a Pandas-on-Spark DataFrame returns a standard pandas DataFrame, not a Pandas-on-Spark DataFrame. The method is designed to collect a small number of rows to the driver for immediate inspection, so it converts the result to a local pandas object. Keeping it as a distributed DataFrame would defeat the purpose of a quick peek.
- ✓
A standard pandas DataFrame containing the first 10 rows.
Why this is correct
This is correct. In Pandas API on Spark, `head(n)` collects the first n rows from the distributed DataFrame and returns them as a local pandas DataFrame. This allows you to inspect a small sample without triggering a full distributed computation. The operation is efficient because it only processes the necessary partitions and brings a limited amount of data to the driver.
- ✗
A list of Row objects containing the first 10 rows.
Why it's wrong here
This is incorrect because `head(10)` does not return a list of Row objects. While that might be the behavior of some other Spark methods, Pandas API on Spark aims to mimic pandas semantics, where `head` returns a DataFrame. The result is a pandas DataFrame, not a list, to maintain compatibility with existing pandas code and expectations.
- ✗
A Spark DataFrame containing the first 10 rows.
Why it's wrong here
This is incorrect because `head(10)` on a Pandas-on-Spark DataFrame returns a local pandas DataFrame, not a Spark DataFrame. Although the underlying data is distributed, the `head` method is a convenience function that collects a small number of rows to the driver. Returning a Spark DataFrame would not align with the pandas API design.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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