Databricks-ML-Pro Model Deployment Practice Question
A machine learning engineer is deploying a model to Databricks Model Serving and wants to implement a blue-green deployment strategy. They have registered two model versions in Unity Catalog: version 1 (current production) and version 2 (new candidate). They want to route 10% of traffic to version 2 for testing while keeping 90% on version 1. Which feature should they use to achieve this?
⚠ Common exam trap
Test-takers frequently confuse model registry aliases or stages with traffic splitting; aliases switch all traffic, while traffic splitting within an endpoint allows gradual rollout.
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 the endpoint with two served entities and use traffic splitting percentages.
Databricks Model Serving allows multiple served entities per endpoint, each with a traffic percentage. This enables canary or blue-green deployments by routing a portion of traffic to a new model version while the rest goes to the stable version. Other methods like separate endpoints or aliases do not provide built-in percentage-based splitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create two separate endpoints and use a load balancer to distribute traffic based on weights.
Why it's wrong here
While creating two endpoints and using an external load balancer could achieve traffic splitting, it is not the native Databricks Model Serving approach. It adds complexity and external dependencies. Databricks provides built-in traffic splitting within a single endpoint, making this method unnecessary and less integrated.
- ✗
Use the model registry's stage transitions to mark version 2 as 'Staging' and version 1 as 'Production', then enable automatic traffic mirroring.
Why it's wrong here
Model registry stages (Staging, Production) are for model lifecycle management but do not control traffic splitting in Model Serving. There is no automatic traffic mirroring based on stages. Traffic splitting must be configured explicitly on the serving endpoint. Stages alone do not route traffic.
- ✓
Configure the endpoint with two served entities and use traffic splitting percentages.
Why this is correct
Databricks Model Serving supports serving multiple model versions within a single endpoint by defining multiple served entities. Each served entity can be assigned a percentage of traffic. By setting version 1 to 90% and version 2 to 10%, the engineer achieves a blue-green or canary deployment. This allows safe testing of the new version.
- ✗
Deploy version 2 as a separate endpoint and use Unity Catalog aliases to switch traffic instantly.
Why it's wrong here
Unity Catalog aliases (e.g., 'Champion') can point to a specific model version, but switching an alias changes all traffic to that version; it does not support percentage-based splitting. Deploying a separate endpoint and switching aliases would result in 100% traffic shift, not a gradual 10% split.
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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.