Databricks-ML-Pro Model Deployment Practice Question
An ML engineer wants to ensure that only models that have passed a specific validation suite can be assigned the 'Champion' alias in Unity Catalog. What is the recommended way to automate this process?
⚠ Common exam trap
Candidates frequently select manual UI actions or legacy workspace notebooks instead of automated Databricks Workflows combined with the MLflow Client API for governed production promotions.
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
✓
Using a Databricks Workflow to run validation and then calling the MLflow Client to update the alias.
Automation in the model lifecycle is best achieved using Databricks Workflows or CI/CD pipelines. By creating a task that runs a validation notebook, the system can programmatically update model aliases via the MLflow Client API only after all tests pass. This ensures a consistent, governed promotion process that prevents low-quality models from reaching production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually checking the validation results and updating the alias in the UI.
Why it's wrong here
Manual updates are prone to human error and lack the auditability and repeatability required for modern MLOps. Relying on manual intervention slows down the deployment cycle and makes it difficult to maintain a consistent standard across multiple models and teams within an organization.
- ✓
Using a Databricks Workflow to run validation and then calling the MLflow Client to update the alias.
Why this is correct
A Databricks Workflow can orchestrate the entire process: loading the new model version, running a suite of performance and bias tests, and then using the `set_registered_model_alias` method if the tests succeed. This provides a fully automated, hands-off, and verifiable path to production.
- ✗
Setting a SQL trigger on the Model Registry table to update the alias automatically.
Why it's wrong here
Unity Catalog Model Registry does not support arbitrary SQL triggers for logic-based alias updates. Furthermore, model validation requires running Python or R code to evaluate metrics, which cannot be efficiently performed within a standard SQL trigger environment intended for simple data manipulations.
- ✗
Configuring the Model Serving endpoint to automatically promote the newest version.
Why it's wrong here
Endpoints are consumers of model versions, not governors of the registry. Automatically promoting the newest version regardless of performance is a dangerous practice that can lead to regressions in production, as it bypasses the essential validation steps required for reliable model deployment.
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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.