Databricks-ML-Pro Model Deployment Practice Question
An ML engineer is deploying a model to Databricks Model Serving and wants to implement A/B testing between two model versions. The engineer needs to route a percentage of traffic to each version and collect performance metrics. Which feature of Databricks Model Serving should the engineer use?
⚠ Common exam trap
The trap here is thinking that MLflow stages or separate endpoints with external load balancers are needed for A/B testing, when Databricks Model Serving provides built-in traffic splitting on a single 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
✓
Create a single endpoint with multiple model versions and configure traffic splitting between them.
To perform A/B testing between model versions, the engineer should create a single Model Serving endpoint that serves multiple model versions with traffic splitting. This allows a specified percentage of requests to be routed to each version, and Databricks provides metrics for each version, facilitating performance comparison. This native feature simplifies A/B testing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy two separate endpoints and use an external load balancer to distribute traffic between them.
Why it's wrong here
While external load balancing is possible, it adds complexity and does not provide integrated metrics for A/B testing. Databricks Model Serving has native support for traffic splitting on a single endpoint, which simplifies management and monitoring. Using separate endpoints is unnecessary and less efficient.
- ✗
Enable the 'Canary' deployment option in the endpoint configuration to automatically split traffic.
Why it's wrong here
Databricks Model Serving does not have a specific 'Canary' deployment option. Traffic splitting is configured by specifying multiple model versions and their traffic percentages. The term 'canary' might refer to a similar concept, but the feature is called traffic splitting, not a separate option.
- ✓
Create a single endpoint with multiple model versions and configure traffic splitting between them.
Why this is correct
Databricks Model Serving supports serving multiple model versions on a single endpoint with traffic splitting. You can specify the percentage of traffic routed to each version, enabling A/B testing. This is the built-in feature for such scenarios, allowing you to compare performance and metrics.
- ✗
Use MLflow's model registry to assign a stage to each model version and route traffic based on the stage.
Why it's wrong here
MLflow stages (e.g., Staging, Production) are used for model lifecycle management but do not control traffic routing in Model Serving. While you can deploy models from different stages, traffic splitting is configured at the endpoint level, not through stages.
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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.