Databricks-ML-Pro ML Ops Practice Question
You need to ensure that a model deployed to a Databricks Model Serving endpoint can be rolled back quickly if it starts performing poorly. Which feature should you use?
⚠ Common exam trap
The trap here is thinking that changing a model version's stage in the registry automatically updates the serving endpoint, but stage transitions and endpoint configurations are separate.
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
✓
Enable multiple model versions on the endpoint and use traffic splitting to shift traffic back to a previous version.
Model Serving endpoints can host multiple model versions and use traffic splitting to route requests. If a new version underperforms, you can instantly shift traffic back to a previous version by adjusting the split, achieving a fast rollback without redeployment. Other methods are manual, slow, or do not directly control the serving endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use MLflow Model Registry to transition the model version back to Staging, which automatically reverts the serving endpoint.
Why it's wrong here
Transitioning a model version in the registry does not automatically update a serving endpoint. The endpoint serves whatever model version is configured, and changing the stage in the registry does not trigger a redeployment. Rollback requires updating the endpoint configuration, so this approach does not provide the quick rollback needed.
- ✗
Delete the current model version from the registry and register the previous version as a new version.
Why it's wrong here
Deleting and re-registering is a disruptive and manual process that does not provide quick rollback. It also loses the lineage and metadata of the deleted version. This approach is error-prone and slow, and it does not leverage the built-in capabilities of Model Serving for traffic management.
- ✗
Configure the endpoint to use a webhook that triggers a rollback job when performance metrics drop.
Why it's wrong here
While webhooks can trigger jobs, this approach requires building custom logic to detect performance drops and then update the endpoint. It is more complex and slower than using built-in traffic splitting. The endpoint itself does not automatically roll back based on metrics without additional infrastructure, so this is not the simplest or fastest method.
- ✓
Enable multiple model versions on the endpoint and use traffic splitting to shift traffic back to a previous version.
Why this is correct
Databricks Model Serving supports serving multiple model versions on a single endpoint and allows you to configure traffic splits. If the new version performs poorly, you can quickly shift traffic back to the previous version without redeploying, enabling fast rollback. This is the recommended approach for safe deployments and rollbacks.
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.