Databricks-ML-Assoc ML Workflows Practice Question
A team registers a model in Unity Catalog as main.ml.churn_model and wants production scoring jobs to always load the newest approved version without editing job code when a new version is promoted. The team uses the MLflow Python client inside a Databricks job. Which model URI should the scoring code use?
⚠ Common exam trap
The trap here is carrying over legacy stage names like Production into Unity Catalog URIs, when Unity Catalog models are promoted through aliases instead.
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
✓
models:/main.ml.churn_model@champion
Unity Catalog registered models support aliases such as champion, which act as movable pointers to a version. Referencing the model with models:/catalog.schema.model@alias lets consumers load whatever version currently holds the alias, so promotion is a metadata operation rather than a code change. Numeric versions and run IDs are immutable, and stage-based URIs belong to the legacy workspace registry rather than Unity Catalog.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
runs:/<run_id>/model
Why it's wrong here
A runs:/ URI points at the model artifact of one specific MLflow run. It is immutable and tied to that run, so it cannot track promotions and would require editing the job every time a new model is trained and approved. It also bypasses the registered model entirely, discarding governance and alias semantics the team wants.
- ✗
models:/main.ml.churn_model/Production
Why it's wrong here
Stage-based URIs such as /Production are part of the legacy Model Registry staging concept. Unity Catalog model names do not use stages, and Databricks recommends aliases instead. Requesting this URI against a Unity Catalog model will not resolve to an approved version and will not automatically follow promotions, so the scoring job would break or pin incorrectly.
- ✓
models:/main.ml.churn_model@champion
Why this is correct
Unity Catalog model aliases are referenced with the @alias syntax, and the alias is a mutable pointer that promotion workflows update. When the team assigns the champion alias to a new version, the scoring job automatically resolves to that version on its next run without any code change. This satisfies the promotion-without-edit requirement and is the recommended pattern for Unity Catalog models.
- ✗
models:/main.ml.churn_model/3
Why it's wrong here
A numeric suffix pins the load to a specific version number. This is valid syntax and will load that exact version, but it never follows promotions: when the team promotes version 4 to production, the job continues scoring with version 3 until someone edits the code. That directly violates the requirement to avoid code changes on promotion.
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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.