Databricks-ML-Pro ML Ops Practice Question
A machine learning engineer needs to schedule a Databricks job that runs a Python wheel task to execute a packaged training pipeline on a recurring basis. The pipeline code is built into a wheel and stored in Unity Catalog volumes. Which job configuration correctly executes this workload?
⚠ Common exam trap
The trap here is treating any method that installs a wheel as equivalent to a Python wheel task, when only the wheel task uses the packaged entry point directly.
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
✓
Create a job with a Python wheel task, specifying the wheel file location and the entry point as package_name.module_name, and attach a cluster or serverless compute.
A Python wheel task is the purpose-built job task type for running packaged Python code: you point it at the wheel and a fully qualified entry point, and Databricks handles installation and invocation. Notebook-based installs or subprocess calls, and Spark submit tasks, do not provide the same reproducible, entry-point-driven execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a job with a notebook task that runs %pip install on the wheel and then calls the pipeline via subprocess from within the notebook.
Why it's wrong here
Invoking the pipeline through subprocess from a notebook bypasses Databricks task orchestration and makes logging, retries, and parameter passing awkward. While %pip install can install a wheel, wrapping execution in a subprocess is not the wheel-task feature and does not provide the clean entry-point execution the scenario requires.
- ✗
Create a job with a Spark submit task that passes the wheel as a --py-files argument and runs the training script as the main class.
Why it's wrong here
Spark submit tasks expect a main class or application JAR and are oriented toward JVM or Spark application entry points, not Python wheel entry points. Using --py-files distributes Python files but does not install the wheel with its dependency metadata, so this is not the supported way to run a packaged Python wheel pipeline.
- ✗
Create a job with a notebook task that contains a single cell calling dbutils.library.install on the wheel path, then imports the training module.
Why it's wrong here
Installing a wheel at runtime inside a notebook task works but is not the packaged wheel-task pattern; it couples execution to notebook code and library installs at run time, which is slower and less reproducible. The scenario calls for running a packaged wheel task, so a notebook that installs and imports the module is not the intended configuration.
- ✓
Create a job with a Python wheel task, specifying the wheel file location and the entry point as package_name.module_name, and attach a cluster or serverless compute.
Why this is correct
A Python wheel task is designed exactly for this: you provide the wheel path and a fully qualified entry point such as package.module, and Databricks installs the wheel on the compute before invoking the entry point. This yields reproducible, versioned execution of the packaged training pipeline on a recurring schedule.
About these practice questions
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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-ML-Pro 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-ML-Pro exam.