Databricks-GenAI-Assoc Governance Practice Question
A GenAI engineer is using MLflow to track experiments for a fine-tuned language model. The engineer wants to ensure that model artifacts and parameters are governed by Unity Catalog, so that access can be controlled and audited. Which Unity Catalog object should the engineer use to register the model?
⚠ Common exam trap
Watch out — candidates often confuse volumes or tables with the model registration object, but only registered models provide model-specific governance.
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
✓
A Unity Catalog registered model
Unity Catalog registered models are the correct object for governing machine learning models. They provide a three-level namespace and support fine-grained access control, versioning, and auditing. By registering the fine-tuned model as a Unity Catalog model, the engineer can manage access and track usage, satisfying the governance requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Unity Catalog schema
Why it's wrong here
A schema is a container for tables, views, volumes, and models, but it is not the object that represents a specific model. While a model must be created within a schema, the schema itself does not provide model-level governance. The engineer needs to register the model as a model object within the schema to apply access controls and auditing.
- ✓
A Unity Catalog registered model
Why this is correct
A Unity Catalog registered model is the object designed for governing machine learning models. It provides a three-level namespace, versioning, and the ability to grant privileges such as `EXECUTE` and `MANAGE`. By registering the model in Unity Catalog, the engineer ensures that access is controlled and audited, meeting the governance requirement.
- ✗
A Unity Catalog volume
Why it's wrong here
Volumes are used to store and govern non-tabular data files, such as model artifacts, but they are not the object used to register a model for governance. Registering a model in Unity Catalog creates a model object that can have versions and aliases, and is separate from the volume where artifacts may be stored. Volumes alone do not provide model-level access control or lineage.
- ✗
A Unity Catalog table
Why it's wrong here
Tables are for structured data, not for registering machine learning models. While model artifacts can be stored in volumes, a table is not the appropriate object for model governance. Unity Catalog models are distinct from tables and provide model-specific features like versioning and aliases. Using a table would not provide the necessary model governance capabilities.
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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.