Databricks-Spark-Assoc Pandas API on Spark Practice Question
A developer is working with a Pandas API on Spark DataFrame `psdf` and wants to perform operations that are efficient in a distributed environment. Which two operations are considered efficient and do not require collecting data to the driver? (Choose two.)
⚠ Common exam trap
The trap here is assuming that any pandas-like method is equally distributed, when methods like `apply` and `to_pandas` actually break distribution and collect data to the driver.
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
✓
`psdf.merge(other_psdf, on='id')`
Distributed operations like `groupby().agg()` and `merge()` execute in parallel across the cluster and return distributed DataFrames, avoiding driver collection. In contrast, `apply` with a lambda, `head`, and `to_pandas` either collect data to the driver or force row-by-row processing, making them less efficient for large-scale data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
`psdf.to_pandas()`
Why it's wrong here
`to_pandas()` collects the entire DataFrame to the driver, which is highly inefficient for large datasets and can cause OutOfMemoryError. It is the opposite of a distributed operation. This is not an efficient operation for large data and should be avoided unless the dataset is small enough to fit in driver memory.
- ✓
`psdf.merge(other_psdf, on='id')`
Why this is correct
`merge` in Pandas API on Spark is implemented as a distributed join in Spark. It shuffles data across the cluster but processes it in parallel, and the result is a distributed DataFrame. This is an efficient operation for large datasets, as it leverages Spark's join optimizations. No data is collected to the driver.
- ✗
`psdf.head(20)`
Why it's wrong here
`head(20)` collects the first 20 rows to the driver as a pandas DataFrame. While it is a small collection, it still brings data to the driver and is not a purely distributed operation. It is efficient in terms of data volume but does not keep the result distributed. Therefore, it is not considered an efficient distributed operation in the same sense as aggregations or joins.
- ✗
`psdf['value'].apply(lambda x: x * 2)`
Why it's wrong here
Using `apply` with a Python lambda on a Series forces row-by-row execution, which is slow and may collect data to the driver or cause serialization overhead. In Pandas API on Spark, `apply` is not vectorized and can lead to performance degradation. It is not considered an efficient distributed operation compared to built-in functions.
- ✓
`psdf.groupby('category').agg({'value': 'sum'})`
Why this is correct
`groupby` followed by `agg` in Pandas API on Spark is translated into a distributed aggregation in Spark. It performs a shuffle but processes data in parallel across the cluster, and the result remains a distributed DataFrame. No data is collected to the driver unless explicitly requested. This is an efficient operation for large datasets.
Quick reference
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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.