Databricks-ML-Pro Model Deployment Practice Question
An ML engineer is updating a model serving endpoint to use a new model version. They want to gradually shift traffic from the old version to the new version to monitor performance before full rollout. Which feature of Databricks Model Serving should they use?
⚠ Common exam trap
The trap here is thinking that aliases or separate endpoints automatically handle gradual traffic shifting, when in fact traffic splitting must be explicitly configured on the endpoint.
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
✓
Configure traffic splitting on the existing endpoint by specifying a percentage of traffic to route to each served model version.
Databricks Model Serving allows configuring traffic splitting on a single endpoint by assigning a percentage of traffic to each served model version. This enables gradual rollout, allowing the team to monitor the new version's performance while still serving the old version. It is the native and recommended approach for safe model 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.
- ✓
Configure traffic splitting on the existing endpoint by specifying a percentage of traffic to route to each served model version.
Why this is correct
Databricks Model Serving supports traffic splitting, allowing you to route a percentage of requests to different model versions within the same endpoint. By specifying the traffic percentage for each served entity, you can gradually shift traffic from the old to the new version. This enables safe rollout and performance monitoring without disrupting the endpoint.
- ✗
Create a new endpoint for the new model version and use a load balancer to distribute traffic.
Why it's wrong here
Creating a separate endpoint and using an external load balancer is not a native feature of Databricks Model Serving. It adds complexity and does not leverage the built-in traffic splitting capabilities. The scenario requires a gradual shift within the serving endpoint, which is supported directly through traffic configuration, making this approach unnecessary and less integrated.
- ✗
Use the 'Champion' and 'Challenger' aliases in Unity Catalog to automatically route traffic based on model performance.
Why it's wrong here
Aliases in Unity Catalog are used for model versioning and deployment, but they do not automatically route traffic based on performance. Traffic splitting must be explicitly configured on the serving endpoint. While aliases can be referenced when updating the endpoint, they do not provide automatic performance-based routing, so this option does not meet the requirement.
- ✗
Enable A/B testing by deploying the new model to a separate endpoint and using Databricks SQL to compare metrics.
Why it's wrong here
Deploying to a separate endpoint and comparing metrics via Databricks SQL is a manual process and does not provide gradual traffic shifting within a single endpoint. Databricks Model Serving has built-in traffic splitting for this purpose. Using separate endpoints would not allow seamless gradual rollout and could increase management overhead.
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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.