Databricks-ML-Assoc Model Deployment Practice Question
An ML engineer is building an automated CI/CD pipeline that, after validating a new model version, must programmatically move it to the `champion` alias in Unity Catalog so the production Model Serving endpoint begins using it. The pipeline runs in a Databricks job using a service principal. Which action accomplishes the promotion programmatically?
⚠ Common exam trap
The trap here is conflating endpoint inference APIs with registry management APIs, leading to the belief that a prediction call can change which model version is served.
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
✓
Call the MLflow client method `set_registered_model_alias` with the model name, alias `champion`, and the target version, using a client connected to the Databricks registry URI.
Programmatic promotion in Unity Catalog is done through the MLflow client's alias APIs: `set_registered_model_alias` moves the alias to the validated version, and any serving endpoint resolving that alias adopts the change. Endpoint inference calls, custom Delta lookup tables, and delete-and-recreate maneuvers do not perform an atomic alias update and either fail outright or introduce availability risk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the model version number into a Delta table that the endpoint reads at request time to decide which version to load.
Why it's wrong here
Serving endpoints do not read arbitrary Delta tables to resolve which model version to load; served entities are resolved from registry metadata. Introducing a custom lookup table would require bespoke code the endpoint does not run, and it bypasses the registry's consistency guarantees. This does not accomplish promotion and would not change what the endpoint serves.
- ✓
Call the MLflow client method `set_registered_model_alias` with the model name, alias `champion`, and the target version, using a client connected to the Databricks registry URI.
Why this is correct
The MLflow client exposes `set_registered_model_alias`, which updates the alias pointer in the registry. When the client is configured with the Databricks tracking URI and the service principal's credentials, the call moves `champion` to the validated version, and the serving endpoint that resolves that alias picks it up. This is the supported programmatic promotion path.
- ✗
Delete the current `champion` alias and recreate it by registering the model again under the same name with a new version number.
Why it's wrong here
Re-registering creates an additional model version rather than promoting an existing validated one, and deleting the alias temporarily leaves the endpoint unable to resolve its served entity, risking failed requests. Promotion should move the alias pointer to the already-validated version, not create duplicate versions. This approach is wasteful and introduces an availability gap during the delete-and-recreate window.
- ✗
Invoke the endpoint's REST API with an update payload that includes the new model version number, forcing the endpoint to reload the model.
Why it's wrong here
The serving endpoint's inference API accepts prediction requests, not arbitrary model version reassignments. Changing what an endpoint serves is a configuration operation, and if the endpoint follows an alias, promotion belongs in the registry rather than in an inference call. This approach misunderstands the separation between registry metadata and endpoint configuration, and would not update the served entity.
Visual reference
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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-Assoc 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-Assoc exam.