Databricks-ML-Pro Model Deployment Practice Question
An ML engineer has a Databricks Model Serving endpoint that is currently serving a registered model version. A new model version is registered in Unity Catalog and must be rolled out to the endpoint without any downtime. Which approach should the engineer use to safely transition traffic to the new model version?
⚠ Common exam trap
The trap here is assuming that any change to the served model requires endpoint recreation or a new endpoint, when in fact updating the served entities of the existing endpoint triggers a zero-downtime rolling update.
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 served entities of the endpoint to point to the new model version and rely on Databricks to perform a rolling update.
Updating the served entities of an existing endpoint is the supported method for zero-downtime model updates in Databricks Model Serving. The platform handles the rolling update by provisioning new resources with the updated model version and shifting traffic only when ready, so the endpoint remains available throughout the process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Modify the model version in Unity Catalog by overwriting the existing version with the new model artifacts.
Why it's wrong here
Unity Catalog model versions are immutable; you cannot overwrite an existing version. The correct approach is to register a new version and update the served entity to reference it. Attempting to modify an existing version is not supported and would not achieve a safe rollout.
- ✗
Create a new endpoint with the new model version and manually update the client applications to point to the new endpoint URL.
Why it's wrong here
Creating a separate endpoint and changing client applications introduces operational overhead and does not provide an atomic traffic shift. Clients may still be pointing to the old endpoint during the transition, and the old endpoint remains active, consuming resources unnecessarily.
- ✓
Update the served entities of the endpoint to point to the new model version and rely on Databricks to perform a rolling update.
Why this is correct
Databricks Model Serving supports updating the served entities of an existing endpoint to reference a new model version. The platform performs a zero-downtime rolling update, provisioning new capacity with the updated model before shifting traffic, so in-flight requests continue to be served by the old version until the new one is ready.
- ✗
Delete the existing endpoint and recreate it with the new model version.
Why it's wrong here
Deleting the endpoint first causes downtime because the endpoint must be recreated and provisioned, during which no requests can be served. This violates the no-downtime requirement and also loses any endpoint configuration such as scaling settings or inference table configuration.
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.