Databricks-Spark-Assoc Pandas API on Spark Practice Question
You are using Pandas API on Spark to process a large dataset. You have a Pandas-on-Spark DataFrame `psdf` and you apply a custom Python function using `psdf.apply(func, axis=1)`. The function is computationally intensive and you notice that the job is running slowly with many tasks. What is the most likely reason for the performance issue?
⚠ Common exam trap
The trap here is assuming that `apply` is as efficient as in pandas, but in a distributed setting, row-wise Python UDFs are slow.
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 `apply` function with `axis=1` is executed row-by-row using a Python UDF, which incurs high serialization and execution overhead, and prevents Spark from optimizing the query.
Using `apply` with `axis=1` in Pandas API on Spark often results in poor performance because it is implemented via a Python UDF that processes rows one by one. This incurs high serialization overhead and prevents Spark's optimizer from improving the plan. For large datasets, it is better to use vectorized operations or built-in functions.
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 `apply` function with `axis=1` is executed row-by-row using a Python UDF, which incurs high serialization and execution overhead, and prevents Spark from optimizing the query.
Why this is correct
This is correct. When you use `apply` with `axis=1`, Pandas API on Spark translates it into a Python UDF that processes each row individually. This involves serializing each row from the JVM to Python, executing the function, and deserializing the result. This overhead is significant and prevents Spark's Catalyst optimizer from optimizing the logic. It is generally recommended to avoid row-wise `apply` for large datasets.
- ✗
The `apply` function with `axis=1` is executed in a distributed manner, but it requires a full shuffle of the data before applying the function.
Why it's wrong here
This is incorrect because `apply` with `axis=1` does not inherently require a shuffle. It operates on each row independently within each partition. The performance issue is not due to shuffling but rather due to the row-by-row Python execution overhead. Shuffling would only occur if the function itself triggers operations that require it.
- ✗
The `apply` function with `axis=1` forces the entire DataFrame to be collected to the driver, and then applies the function locally, causing memory issues.
Why it's wrong here
This is incorrect because `apply` with `axis=1` does not collect the entire DataFrame to the driver. It applies the function in a distributed manner across partitions. Collecting to the driver would only happen if you explicitly call `toPandas()` or similar. The slowness is due to per-row Python execution, not data collection.
- ✗
The `apply` function with `axis=1` is not supported in Pandas API on Spark and falls back to a single-node pandas execution, causing a bottleneck.
Why it's wrong here
This is incorrect because `apply` with `axis=1` is supported in Pandas API on Spark. It is executed in a distributed manner by applying the function to each partition. However, it can be slow due to Python UDF overhead. It does not fall back to single-node execution unless the data is collected to the driver.
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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.