Databricks-ML-Pro Model Deployment Practice Question
An ML engineer needs to deploy a model to Databricks Model Serving. The model was logged with MLflow and registered in Unity Catalog. The engineer wants to ensure that only the latest version of the model is served and that the endpoint can be updated without downtime. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that MLflow stages or aliases automatically update serving endpoints, or that recreating an endpoint is necessary for updates.
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
✓
Update the existing endpoint to serve the new model version using the Databricks UI or REST API, which performs a rolling update.
Updating an existing endpoint to a new model version via the UI or REST API triggers a rolling update, ensuring zero downtime and keeping the same endpoint URL. This is the standard method for deploying a new version without disrupting clients.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the existing endpoint and create a new one with the same name and configuration, pointing to the new model version.
Why it's wrong here
Deleting and recreating an endpoint causes downtime and may change the endpoint URL, disrupting clients. Databricks Model Serving supports in-place updates that preserve the endpoint URL and provide zero-downtime deployment. This is the preferred method for updating a served model version.
- ✗
Use MLflow's transition request to move the model to Production stage, which automatically updates the serving endpoint.
Why it's wrong here
MLflow stages are not integrated with Databricks Model Serving in Unity Catalog. Moving a model to a stage does not automatically update an endpoint. Unity Catalog uses aliases instead of stages, and endpoint updates must be explicitly triggered. Stages are part of the Workspace Model Registry, not Unity Catalog.
- ✗
Create a new endpoint for each model version and update the client application to point to the new endpoint URL.
Why it's wrong here
Creating a new endpoint for each version requires manual client updates and does not provide a seamless update process. It also leads to endpoint proliferation and management overhead. Databricks Model Serving supports updating an existing endpoint with a new model version, which is the recommended approach for zero-downtime updates.
- ✓
Update the existing endpoint to serve the new model version using the Databricks UI or REST API, which performs a rolling update.
Why this is correct
Databricks Model Serving allows you to update an endpoint to a new model version without downtime. The service performs a rolling update, gradually shifting traffic to the new version while maintaining availability. This ensures that the latest version is served and clients continue to use the same endpoint URL.
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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.