Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
When deploying a model endpoint, which Databricks feature provides the capability to review and approve the model before it is promoted to production?
⚠ Common exam trap
Test-takers frequently confuse workspace-level permissions or generic Git pull requests with Unity Catalog's formal model lifecycle stage transitions.
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
✓
Unity Catalog Model Status Transitions
The Unity Catalog Model Registry provides a formal lifecycle management process, including status transitions such as 'Staging', 'Production', and 'Archived'. By using these status labels, organizations can implement a human-in-the-loop approval process where models must pass specific validation criteria—or manual reviews—before they are eligible for the production inference endpoint. This promotes safety, quality, and compliance in machine learning deployments by ensuring only vetted models reach end-users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Live Tables Quality Checks
Why it's wrong here
Delta Live Tables quality checks are for data validation within ETL pipelines. While they ensure that data is clean, they do not manage the lifecycle or approval process for machine learning models. Using them for model promotion would be an misuse of the feature's intended purpose.
- ✓
Unity Catalog Model Status Transitions
Why this is correct
Model status transitions in Unity Catalog allow teams to manage a model's lifecycle through stages like 'Staging' and 'Production'. This is the standard mechanism to control which models are available for production serving, enabling teams to enforce rigorous evaluation and approval gates before promoting a new model version.
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Databricks Job Task Notifications
Why it's wrong here
Job notifications are for alerting users about task status (e.g., success, failure). They do not provide a mechanism for approval, status tracking, or artifact promotion. Relying on email or Slack alerts for model approval is not a governed or reproducible process for managing model deployment lifecycles.
- ✗
Git branch protection rules
Why it's wrong here
Git branch protection ensures code quality, but it does not track the status of registered model artifacts within the Databricks platform. Even if code is merged, the model itself must be formally transitioned within the Model Registry to be available for production serving via the endpoint.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-GenAI-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-GenAI-Assoc exam.