Databricks-ML-Pro Model Deployment Practice Question
A team is deploying a model to Databricks Model Serving and wants to implement a canary release strategy to gradually shift traffic from the current model version to a new version. Which TWO configurations are required to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is thinking that canary deployments require multiple endpoints or automatic flags, when actually they are configured within a single endpoint using multiple served entities and explicit traffic weights.
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 serving endpoint with multiple served entities, each referencing a different model version.
Canary releases in Databricks Model Serving are achieved by configuring a single endpoint with multiple served entities, each pointing to a different model version. Traffic is then split between these entities using the endpoint's traffic configuration, which can be updated via the REST API. This allows gradual shifts and monitoring.
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 a serving endpoint with multiple served entities, each referencing a different model version.
Why this is correct
Databricks Model Serving supports multiple served entities within a single endpoint, each pointing to a different model version. This allows you to route traffic to different versions, which is essential for canary releases. You can then adjust the traffic split between the entities to gradually shift traffic.
- ✗
Deploy two separate endpoints and use an external load balancer to distribute traffic between them.
Why it's wrong here
While external load balancing can distribute traffic, it is not a native Databricks Model Serving feature and adds complexity. It also does not provide integrated monitoring and traffic management within Databricks. The recommended approach is to use a single endpoint with multiple served entities and traffic configuration.
- ✗
Enable automatic canary deployment by setting a flag in the model's MLflow metadata.
Why it's wrong here
There is no automatic canary deployment flag in MLflow metadata. Canary releases require manual configuration of traffic splits and multiple served entities. Relying on such a flag would not work because the feature does not exist; you must explicitly configure the endpoint and traffic settings.
- ✗
Configure the endpoint to use a single served entity and rely on the model's internal logic to route requests based on a random seed.
Why it's wrong here
Using a single served entity with internal routing logic does not provide a true canary release because both versions would need to be packaged into one model, which is not standard. It also lacks the ability to monitor and adjust traffic splits at the endpoint level. This approach is not supported for canary deployments in Model Serving.
- ✓
Use the Databricks REST API to update the traffic configuration for the endpoint, specifying the percentage of traffic for each served entity.
Why this is correct
The traffic configuration for a serving endpoint can be updated via the Databricks REST API (e.g., using the update endpoint configuration). By specifying the percentage of traffic for each served entity, you can implement a canary release. This allows dynamic adjustment without redeploying the endpoint.
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.