Databricks-ML-Assoc Model Deployment Practice Question
A data scientist has registered a scikit-learn model in Unity Catalog as `ml_team.prod.churn_model` and wants it served by a Databricks Model Serving endpoint that performs online inference for a web app. The endpoint must automatically pick up new model versions as they are promoted, without the data scientist editing the endpoint each time. Which approach should the data scientist use to configure the served entity?
⚠ Common exam trap
The trap here is assuming that any serving configuration referencing a registered model will automatically follow the newest version, when in fact only an alias or version reference determines what is actually 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
✓
Serve the model by name with the version set to the alias `champion`, so the endpoint resolves the alias to whatever version it currently points to.
Serving by model name with an alias makes the endpoint follow promotions automatically: when a new version is assigned the `champion` alias, subsequent requests resolve to that version. Pinning a numeric version, copying artifacts to DBFS, or polling with a job all require manual or scheduled intervention and cannot deliver the immediate, configuration-free update the scenario demands.
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 by name with the version set to the alias `champion`, so the endpoint resolves the alias to whatever version it currently points to.
Why this is correct
Serving a model by name plus an alias lets Model Serving resolve the alias at request time, so promoting a new version to `champion` updates the served model without endpoint edits. This matches the requirement of automatic pickup on promotion and works with Unity Catalog-registered models, provided the endpoint's identity has EXECUTE on the model and its versions.
- ✗
Create a Databricks job that runs every five minutes, downloads the latest model version, and calls the Model Serving REST API to overwrite the endpoint configuration with the new numeric version.
Why it's wrong here
A periodic job that rewrites the endpoint configuration adds latency between promotion and serving, requires compute and credentials, and risks race conditions during redeploys. It also duplicates functionality that aliases already provide natively. This is operationally heavier and less reliable than serving an alias, and it briefly disrupts the endpoint on every update.
- ✗
Serve the model by name with the version pinned to the numeric version that is currently in production, then re-create the endpoint whenever a new version is registered.
Why it's wrong here
Pinning to a numeric version freezes the endpoint to that exact artifact, so a newly promoted version is never served until someone edits or recreates the endpoint. That directly violates the requirement for automatic pickup. Numeric pinning is appropriate for reproducible rollbacks, but not for a continuously promoted production model that must follow the latest champion.
- ✗
Export the MLflow model to a local directory, upload the artifacts to DBFS, and configure the endpoint to load the model from that DBFS path.
Why it's wrong here
Model Serving endpoints are configured with registered model entities or serving features, not raw DBFS artifact paths. Even if artifacts live in cloud storage, the endpoint needs registry metadata, signatures, and dependency information to build the serving container. Manually copying artifacts bypasses lineage and versioning and cannot satisfy automatic promotion-driven updates.
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.