Databricks-ML-Pro Model Deployment Practice Question
An ML engineer has deployed a model to Databricks Model Serving and wants to update the endpoint to serve a new model version without changing the endpoint URL or causing downtime. Which approach is correct?
⚠ Common exam trap
The trap here is believing that model registry stage transitions or signature changes automatically propagate to serving endpoints.
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's configuration to reference the new model version and apply the update.
To update a serving endpoint to a new model version without downtime, you update the endpoint's configuration to reference the new version and apply the change. Databricks handles the rolling update, ensuring the endpoint URL remains unchanged. Other options would either cause downtime, require client changes, or rely on non-existent automatic updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Update the existing endpoint's configuration to reference the new model version and apply the update.
Why this is correct
Databricks Model Serving allows you to update an existing endpoint's configuration, including the model version, via the REST API or UI. The endpoint URL remains the same, and Databricks performs a rolling update to minimize downtime. This is the standard way to deploy a new model version to an existing endpoint without disrupting service.
- ✗
Use MLflow's `transition_model_version_stage` to move the new version to 'Production', which automatically updates the serving endpoint.
Why it's wrong here
Transitioning a model version to a stage does not automatically update a serving endpoint. The endpoint configuration explicitly references a model version. Even if stages were used, they are deprecated in Unity Catalog. You must manually update the endpoint to point to the new version. This option misunderstands the relationship between stages and serving.
- ✗
Create a new endpoint with the new model version, then delete the old endpoint and update DNS to point to the new endpoint.
Why it's wrong here
Creating a new endpoint and deleting the old one would change the endpoint URL, requiring clients to update their configurations. This causes downtime and is not necessary. Databricks Model Serving supports in-place updates, so you do not need to create a new endpoint. This approach is cumbersome and error-prone.
- ✗
Modify the model's signature in Unity Catalog to match the new version, and the endpoint will detect the change and update automatically.
Why it's wrong here
The model signature defines input and output schemas, but changing it does not trigger an automatic update of serving endpoints. Endpoints do not poll for model changes. You must explicitly update the endpoint configuration to reference the new model version. This option invents an automatic detection mechanism that does not exist.
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.