Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is using Spark Connect to connect to a Databricks cluster from a remote Python client. They need to run a custom Python function on a DataFrame column. They define the function and register it as a UDF using spark.udf.register(). After executing the job, they notice that the UDF fails with a ModuleNotFoundError for a library that is installed on their local machine but not on the cluster. What is the most likely cause and the appropriate solution?
⚠ Common exam trap
The trap here is assuming that UDFs run on the client in Spark Connect, when they actually run on the remote cluster.
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 UDF is executed on the cluster, and the required library must be installed on all cluster nodes; the solution is to install the library on the cluster.
In Spark Connect, UDFs are serialized and sent to the Spark server for execution on the cluster. Therefore, any Python libraries used by the UDF must be installed on the cluster nodes. The ModuleNotFoundError indicates that the library is missing on the cluster, so the solution is to install it there, not on the client.
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 UDF is executed on the cluster, and the required library must be installed on all cluster nodes; the solution is to install the library on the cluster.
Why this is correct
This is correct because Spark Connect sends the UDF code to the server, where it is executed on the cluster's executors. Any dependencies used by the UDF must be available on the cluster nodes. The ModuleNotFoundError indicates that the library is not installed on the cluster. The appropriate solution is to install the library on the cluster, either via cluster libraries or init scripts.
- ✗
The UDF is executed on the client, so the library must be installed locally; the error indicates a local environment issue.
Why it's wrong here
This is incorrect because UDFs in Spark Connect are not executed on the client. They are serialized and sent to the Spark server for execution on the cluster. The error occurs because the library is missing on the cluster, not the client. The client's local environment does not affect UDF execution on the server.
- ✗
The UDF is executed in a separate Python process on the client, so the library must be installed in that process; the solution is to add the library to the client's Python path.
Why it's wrong here
This is incorrect because Spark Connect does not execute UDFs in a separate process on the client. The UDF is sent to the server and executed on the cluster. The client's Python path is irrelevant to the server-side execution. The error arises because the library is missing on the cluster, not on the client.
- ✗
The UDF is executed on the driver node only, so the library must be installed on the driver; the solution is to restart the driver with the library.
Why it's wrong here
This is incorrect because UDFs are executed on the executors, not just the driver. While the driver may coordinate, the actual computation happens on executors. The library must be available on all nodes that run the UDF. Installing only on the driver would not resolve the error for distributed execution.
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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.