Databricks-ML-Assoc Model Deployment Practice Question
A data scientist has registered a scikit-learn model in Unity Catalog as `ml_prod.churn.model_v3` and wants the Databricks Model Serving endpoint to automatically pick up newly registered model versions as they are promoted to the `champion` alias. Which configuration should the data scientist use when creating the serving endpoint?
⚠ Common exam trap
The trap here is assuming that a bare registered model name automatically resolves to the latest version, when Databricks Model Serving requires an explicit version or alias-qualified path.
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
✓
Serve the model using the alias-qualified path `ml_prod.churn.model_v3@champion`.
Alias-qualified paths in Unity Catalog let a serving endpoint follow a movable pointer. When a new model version is registered and the `champion` alias is reassigned to it, the endpoint resolves the alias and begins serving the new artifact. This enables zero-touch promotion workflows, keeps governance in Unity Catalog, and avoids endpoint recreation or explicit version pinning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Serve the model using the alias-qualified path `ml_prod.churn.model_v3@champion`.
Why this is correct
Databricks Model Serving supports Unity Catalog alias-qualified model paths. Pointing the endpoint at `model_v3@champion` causes the serving infrastructure to resolve the alias at request time, so when a new version is promoted to the `champion` alias, the endpoint automatically serves the updated artifact without endpoint reconfiguration or downtime.
- ✗
Export the model with `mlflow.sklearn.save_model` to a DBFS path and point the endpoint at that directory.
Why it's wrong here
Serving an artifact from a raw DBFS directory bypasses Unity Catalog governance, lineage, and alias promotion entirely. The endpoint would not automatically track newly registered versions, and the team loses access control and auditability. This approach also requires manual file management and does not integrate with the registry-based promotion workflow the scenario requires.
- ✗
Serve the model by its explicit version number, `ml_prod.churn.model_v3/3`, and re-create the endpoint after each promotion.
Why it's wrong here
Pinning the endpoint to an explicit numeric version freezes it to that artifact. Newly registered versions, even when tagged with the `champion` alias, will never be served because the endpoint resolves the literal version path. Re-creating the endpoint on every promotion introduces downtime and manual toil, defeating the purpose of alias-based promotion workflows in Unity Catalog.
- ✗
Serve the model by its registered name only, `ml_prod.churn.model_v3`, and rely on Databricks to always use the latest version.
Why it's wrong here
A bare registered model name without a version or alias is not a valid serving target for Model Serving endpoints in Unity Catalog. Databricks requires either an explicit version or an alias-qualified path. Relying on ambiguous resolution would also risk silently serving a version that was never validated for production, which is exactly what alias-based promotion prevents.
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.