Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is writing a Spark Connect application that must run a pandas UDF on a remote Databricks cluster. The code uses the spark.conf.set() API to pass a custom Python module to the executors. Which statement describes the correct behavior?
⚠ Common exam trap
The trap here is assuming that spark.conf.set() can be used as a generic code-distribution mechanism because it accepts arbitrary key-value pairs.
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
✓
spark.conf.set() can only set Spark SQL configuration properties; it cannot transfer arbitrary Python modules to executors, so the pandas UDF will fail with an ImportError.
spark.conf.set() is designed exclusively for Spark configuration properties and cannot distribute Python code. In a Spark Connect architecture, the client and executors are decoupled, so any custom Python dependencies used by pandas UDFs must be installed on the cluster as libraries or uploaded to a path that the cluster can access. Setting a configuration value will not make the module available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.conf.set() automatically packages the referenced module and sends it to the executors as part of the Spark Connect session configuration.
Why it's wrong here
spark.conf.set() does not perform any packaging or code distribution. It only updates the Spark configuration map. Spark Connect does not inspect the values for Python module references, so the module will never reach the executors, and the pandas UDF will fail when it tries to import the missing module.
- ✗
The pandas UDF will work because Spark Connect serializes all local Python modules along with the UDF closure and ships them to the remote cluster.
Why it's wrong here
Spark Connect does serialize the UDF closure, but it does not automatically discover and ship every module that the closure imports. If the UDF references a custom module that is not installed on the cluster, the serialized closure will still fail at runtime. Explicit library installation is required.
- ✓
spark.conf.set() can only set Spark SQL configuration properties; it cannot transfer arbitrary Python modules to executors, so the pandas UDF will fail with an ImportError.
Why this is correct
spark.conf.set() is strictly for Spark configuration properties. It has no mechanism to ship a Python module to the remote executors, so any pandas UDF that imports that module will raise an ImportError. To distribute code with Spark Connect, the module must be installed as a cluster library or uploaded to a workspace path.
- ✗
spark.conf.set() can transfer the module only if the module is already present in the Spark Connect client's current working directory.
Why it's wrong here
The working directory of the client has no bearing on module availability on the remote executors. spark.conf.set() does not read files from the client filesystem. Even if the module is in the working directory, it will not be sent to the cluster, and the UDF will raise an ImportError.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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