Databricks-ML-Pro ML Ops Practice Question
A machine learning team is using MLflow Model Registry to manage a model that is deployed to a production endpoint. They need to implement a CI/CD pipeline that automatically transitions a model version from 'Staging' to 'Production' only after it passes a set of validation tests. Which MLflow feature allows them to trigger the transition based on test results?
⚠ Common exam trap
Watch out — candidates often confuse webhooks, which react to transitions, with the API that actually performs the transition on demand.
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
✓
MLflow Model Registry stage transitions via the REST API
The MLflow Model Registry REST API allows programmatic stage transitions. In a CI/CD pipeline, after validation tests pass, you can call the API to move the model version from Staging to Production. This integrates seamlessly with automation tools. Webhooks are for notifications, not for initiating transitions, and other options lack the direct capability to change stages based on test results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
MLflow Model Registry stage transitions via the REST API
Why this is correct
The MLflow Model Registry REST API provides endpoints to transition model versions between stages. A CI/CD pipeline can call these endpoints programmatically after validation tests pass. This allows automated promotion based on test outcomes. The API is the standard way to integrate stage transitions into external workflows.
- ✗
MLflow Projects with conditional steps
Why it's wrong here
MLflow Projects define reproducible runs but do not include conditional logic for stage transitions. They are used for packaging code and dependencies. While you can run a project as part of CI/CD, it does not have built-in mechanisms to transition model stages based on test outcomes. You would still need to use the API.
- ✗
Databricks Jobs with task dependencies
Why it's wrong here
Databricks Jobs can orchestrate tasks with dependencies, but they do not directly interact with the MLflow Model Registry to transition stages. You could write a task that calls the MLflow API, but the feature itself does not provide the transition capability. The core requirement is the API call, not the job orchestration.
- ✗
MLflow Model Registry webhooks
Why it's wrong here
Webhooks in MLflow Model Registry trigger external actions when stage transitions occur, but they do not initiate the transition based on test results. They are reactive to events, not proactive. To transition based on tests, you need to call the API from your CI/CD pipeline. Webhooks alone cannot enforce validation gates.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.