Databricks-ML-Pro ML Ops Practice Question
A machine learning engineer is setting up a CI/CD pipeline for a model deployed to Databricks Model Serving. The pipeline must automatically update the serving endpoint when a new model version is registered in the MLflow Model Registry and passes a validation job. Which Databricks feature should the engineer use to trigger the update?
⚠ Common exam trap
The trap here is assuming Model Serving has an auto-update feature or that polling is sufficient, when event-driven webhooks are the intended mechanism for registry-triggered deployments.
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 webhooks that call a Databricks job to validate and update the endpoint.
MLflow Model Registry webhooks enable event-driven automation. By configuring a webhook on MODEL_VERSION_CREATED, a Databricks job can be triggered to validate the new version and then update the Model Serving endpoint via the REST API. This integrates validation gating and deployment in a CI/CD pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Model Serving's built-in auto-update feature, which automatically deploys the latest model version from the registry.
Why it's wrong here
Databricks Model Serving does not have an auto-update feature that automatically deploys the latest model version from the registry. Deployment is a manual or orchestrated action. This option describes a non-existent capability, so it cannot satisfy the requirement.
- ✓
MLflow Model Registry webhooks that call a Databricks job to validate and update the endpoint.
Why this is correct
Registry webhooks can trigger on MODEL_VERSION_CREATED events, invoking a Databricks job that runs validation and then updates the serving endpoint via the REST API. This provides an event-driven, automated pipeline that meets the requirement of updating upon registration and successful validation.
- ✗
A Databricks notebook with a while loop that continuously checks the Model Registry for new versions and updates the endpoint.
Why it's wrong here
A continuous while loop is inefficient, consumes resources, and is not a recommended pattern for CI/CD. It lacks the event-driven nature of webhooks and can be unreliable. It also does not integrate with validation gating in a clean way, making it a poor choice for production pipelines.
- ✗
Databricks Jobs with a schedule trigger that polls the Model Registry every hour for new versions.
Why it's wrong here
A scheduled job polling the registry introduces latency and is not event-driven. The requirement is to update automatically when a new version is registered and passes validation. Polling would not provide immediate updates and could miss rapid successive registrations. It is not the optimal solution.
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.