Databricks-ML-Assoc ML Workflows Practice Question
An ML platform team is standardizing how models move from experimentation to production on Databricks. They want promotion decisions to be auditable and to prevent unvalidated models from serving live traffic. Which TWO practices align with Databricks Model Registry and Unity Catalog governance? (Choose two.)
⚠ Common exam trap
The trap here is treating MLflow stages as the promotion mechanism for Unity Catalog models, when governed models use aliases and Unity Catalog privileges 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
✓
Store models in Unity Catalog with a three-level name and control access using catalog, schema, and model privileges.
Auditable promotion in Unity Catalog rests on governed model objects and movable aliases. Three-level names plus catalog, schema, and model privileges control who can read, write, and serve models, while champion and challenger aliases record which version is approved for production or evaluation. Together they let consumers reference a stable name that promotion workflows update, with every change visible in the registry and subject to access control.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed promotion logic entirely in notebook widgets so reviewers approve versions from the notebook UI.
Why it's wrong here
Notebook widgets are transient UI state, not durable or auditable approval records, and they do not enforce access control. A promotion approved through a widget leaves no governed trace and can be bypassed by running the notebook with different inputs. The team needs auditable, enforceable promotion, which belongs in registry metadata and privileges rather than notebook parameters.
- ✗
Allow any team member with workspace access to register and promote models to reduce bottlenecks.
Why it's wrong here
Broad, undifferentiated write access undermines auditability and lets unvalidated models be promoted. Governance requires least-privilege grants so only authorized roles can create versions or move the champion alias. The scenario explicitly wants to prevent unvalidated models from serving traffic, and open promotion permissions work against that goal rather than supporting it.
- ✗
Rely on MLflow stages like Staging and Production to gate which model version receives live traffic.
Why it's wrong here
MLflow stages are a legacy workspace-registry concept and are not supported for Unity Catalog models, where aliases and tags replace them. Building promotion gates on stages would not work for governed models and would create a parallel, unauditable mechanism. The team wants Unity Catalog governance, so stage-based gating contradicts the stated direction.
- ✓
Store models in Unity Catalog with a three-level name and control access using catalog, schema, and model privileges.
Why this is correct
Unity Catalog governs models as securable objects, so privileges such as USE CATALOG, USE SCHEMA, and EXECUTE on the model determine who can load or serve them. This gives the platform team auditable, centralized access control instead of relying on workspace-local registry permissions, and it prevents unauthorized consumers from loading unvalidated versions in a governed environment.
- ✓
Use model aliases such as champion and challenger to mark the versions approved for serving and evaluation.
Why this is correct
Aliases are movable named pointers to specific model versions, so promotion becomes an explicit, logged metadata change. A serving endpoint or job that references the champion alias automatically follows promotions, and reviewers can see which version held the alias at any time. This supports auditable promotion while keeping consumer code stable and free of version numbers.
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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.