Databricks-GenAI-Assoc Application Development Practice Question
An organization requires that all GenAI models deployed in Databricks be tracked and managed with a unified registry for compliance. Which feature should the developer use?
⚠ Common exam trap
Candidates frequently confuse legacy MLflow Model Registry workspaces with the organization-wide compliance and governance features provided by the Unity Catalog Model Registry.
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 Registry
Unity Catalog Model Registry serves as the centralized hub for governing, versioning, and deploying machine learning models across an organization. By using the Unity Catalog, developers ensure that every model has a documented lineage, access control, and deployment status. This is critical for regulatory compliance and enterprise security, as it prevents unvetted models from being deployed and provides a transparent audit trail of every model version currently in use.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Git branches to manage different model versions.
Why it's wrong here
Git is excellent for source code version control, but it is not designed to manage the lifecycle or governance of compiled machine learning models. It lacks the metadata, model-specific deployment controls, and lineage tracking features provided by a dedicated model registry, which are necessary for enterprise-wide model governance.
- ✓
Unity Catalog Model Registry
Why this is correct
Unity Catalog Model Registry allows for central management, governance, and deployment of models. It enforces consistent access policies, tracks the lineage of model artifacts, and manages the lifecycle stages of models, ensuring that only approved models reach production while maintaining a clear, auditable trail for compliance requirements.
- ✗
Store model artifacts as Pickle files in a shared workspace folder.
Why it's wrong here
Storing models in shared workspace folders is insecure and lacks version control or lifecycle management. It does not provide the governance, access control, or lineage tracking required for enterprise compliance. This manual approach is highly prone to errors and makes it impossible to track which model version is deployed.
- ✗
Create a custom Python class to track model metadata in a Delta table.
Why it's wrong here
While storing metadata in a Delta table is useful for reporting, it does not replace the functionality of a managed Model Registry. A custom solution lacks the integrated deployment, access control, and model-specific lifecycle features that Unity Catalog provides out-of-the-box, increasing the overhead for maintaining a compliant model inventory.
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.