Databricks-Spark-Assoc Pandas API on Spark Practice Question
You are migrating a legacy Pandas codebase to Databricks using the Pandas API on Spark. You have a DataFrame 'pdf' and need to calculate the average of a column 'revenue' while ensuring the computation remains distributed across the cluster. Which command is the idiomatic approach to achieve this?
⚠ Common exam trap
Candidates often try to convert the DataFrame to a Pandas object using toPandas() before calculating the mean. This pulls all data to the driver, leading to immediate OOM errors on large datasets.
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
✓
pdf['revenue'].mean()
The Pandas API on Spark maintains a familiar syntax while pushing execution to the Spark engine. By calling .mean() on the series, the API translates the operation into a Spark aggregation plan. This is critical for scalability, as it avoids collecting the entire dataset to the driver node, which would cause an OutOfMemory error on large datasets exceeding driver memory capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
pdf.to_pandas().mean()
Why it's wrong here
Calling to_pandas() converts the Spark-backed DataFrame into a local Pandas DataFrame on the driver. For datasets larger than the driver memory, this operation will trigger an OOM error, defeating the purpose of utilizing Spark's distributed processing capabilities for large-scale data analysis tasks.
- ✗
pdf.apply(lambda x: x.mean())
Why it's wrong here
While apply can be used for custom logic, it is less efficient than the built-in vectorized mean function. Using specialized aggregation methods allows the Spark optimizer to perform predicate pushdown and efficient shuffling, resulting in significantly better performance for simple statistical calculations like column averages.
- ✓
pdf['revenue'].mean()
Why this is correct
The Pandas API on Spark implements the standard Pandas Series interface. Calling mean() directly on the column triggers a distributed Spark aggregation. This executes the calculation in parallel across worker nodes, which is the most efficient and idiomatic way to handle distributed numeric data processing.
- ✗
pdf.rdd.map(lambda x: x.revenue).mean()
Why it's wrong here
Accessing the underlying RDD interface is generally discouraged when using the Pandas API on Spark. It breaks the abstraction provided by the DataFrame API, requiring manual handling of data types and serialization, which complicates maintenance and prevents the Catalyst optimizer from optimizing the high-level operation.
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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.