Databricks-Spark-Assoc Pandas API on Spark Practice Question
You have a Pandas API on Spark DataFrame `psdf` that was created from a Spark DataFrame with 200 partitions. You call `psdf.head(10)` in a Databricks notebook. What is the most likely performance characteristic of this operation?
⚠ Common exam trap
The trap here is assuming that any operation on a large partitioned DataFrame must scan all partitions, when in fact limit-based operations like `head` are optimized to read only what is needed.
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
✓
It collects only the necessary rows from the first partition(s) and returns quickly without scanning the entire dataset.
The `head` operation in Pandas API on Spark is designed to be efficient by leveraging Spark's `limit` operator, which reads only the necessary rows from the first partitions. It does not scan the entire dataset or perform a full shuffle. This makes it suitable for quickly inspecting large DataFrames without incurring heavy computation costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It shuffles all data to a single partition before returning the first 10 rows, causing a full data movement.
Why it's wrong here
`head(10)` does not require a shuffle to a single partition. Spark's `limit` operator can collect results from multiple partitions without a full shuffle. While the final result is brought to the driver, the data movement is minimal and not a full shuffle of all 200 partitions. This option overstates the cost and mischaracterizes the execution plan.
- ✗
It fails with an error because `head` is not supported on DataFrames with more than 100 partitions.
Why it's wrong here
There is no partition count limit for `head` in Pandas API on Spark. The operation is supported regardless of the number of partitions. Spark's `limit` can handle any partition count by reading only what is needed. This option invents a restriction that does not exist in the API, making it incorrect for this scenario.
- ✓
It collects only the necessary rows from the first partition(s) and returns quickly without scanning the entire dataset.
Why this is correct
`head(10)` in Pandas API on Spark is optimized to fetch only the required number of rows. Spark executes a `limit` operation that reads from the first partition(s) until 10 rows are collected, avoiding a full scan. This makes it efficient even on large datasets, as it does not process all 200 partitions. The operation returns a small pandas DataFrame to the driver.
- ✗
It triggers a full scan of all 200 partitions to ensure the first 10 rows are correctly ordered.
Why it's wrong here
`head(10)` does not require a full scan; it only needs to retrieve enough rows from the first partitions to satisfy the limit. Spark's lazy evaluation and partition pruning allow it to stop early once 10 rows are collected, so scanning all 200 partitions is unnecessary. This option describes behavior more typical of a global sort or a full aggregation, not a simple head operation.
About these practice questions
This Databricks-Spark-Assoc question is part of Courseiva's 295-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-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.