Databricks-ML-Assoc Model Deployment Practice Question
A team has several model versions registered in Unity Catalog. They want to serve a specific version through a Databricks Model Serving endpoint and later promote a newer version without changing the endpoint URL used by applications. Which approach should they use?
⚠ Common exam trap
The trap here is assuming the endpoint must be rebuilt for each new version, when a Unity Catalog alias lets the same endpoint follow promotions.
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 the endpoint pointing to a Unity Catalog alias such as 'champion', and update the alias to reference the new model version when promoting.
Databricks Model Serving endpoints can reference a Unity Catalog model alias instead of a fixed version. Applications call a stable endpoint URL, and the team promotes new versions by moving the alias, such as from 'champion' to a newer version. This preserves the URL, avoids endpoint recreation, and keeps promotion governed through alias updates rather than client-side changes.
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 the endpoint pointing to a fixed model version, then delete and recreate the endpoint with the new version when promoting.
Why it's wrong here
Pinning the endpoint to a fixed version means promotion requires recreating the endpoint, which changes its URL or causes downtime. Applications would need reconfiguration, defeating the goal of a stable URL. Recreating also disrupts warm capacity and any monitoring tied to the endpoint identity.
- ✓
Create the endpoint pointing to a Unity Catalog alias such as 'champion', and update the alias to reference the new model version when promoting.
Why this is correct
Serving endpoints can target a model alias rather than a fixed version. Pointing the endpoint at an alias like 'champion' lets the team repoint the alias to a new version, and the endpoint picks up the change without altering the URL. This decouples consumers from version numbers and supports controlled promotion.
- ✗
Create the endpoint pointing to the model name without a version, and rely on the platform to always serve the most recently created version.
Why it's wrong here
Endpoints require an explicit version or alias; there is no implicit 'latest version' targeting. Even if such behavior existed, serving the newest version automatically would bypass validation and promotion controls, allowing untested versions to reach production. Explicit versioning or aliases are required for predictable, governed deployment.
- ✗
Create one endpoint per model version and place a load balancer in front that routes to the newest endpoint.
Why it's wrong here
Operating one endpoint per version multiplies cost and operational overhead, and the external load balancer becomes an additional component to manage and secure. The platform's alias mechanism already provides version switching without extra infrastructure, so this approach adds complexity without benefit and still requires clients to be reconfigured.
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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.