Databricks-GenAI-Assoc Governance Practice Question
A GenAI team is using MLflow to track experiments for a large language model. They want to ensure that only team members can view the experiment results and that the experiment artifacts are stored in a governed location. Which Unity Catalog integration should they use to manage MLflow experiments?
⚠ Common exam trap
The trap here is assuming that workspace ACLs or personal access tokens provide sufficient governance for MLflow experiments, when in fact Unity Catalog registration is required for fine-grained access control.
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
✓
Register the MLflow experiment in Unity Catalog and grant appropriate privileges on the experiment object
Registering an MLflow experiment in Unity Catalog makes it a securable object, allowing you to grant privileges to specific groups. Artifacts are stored in a governed location such as a Unity Catalog volume or external location. This provides both access control and governance, meeting the team's requirements.
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 the MLflow tracking server with a personal access token for each team member
Why it's wrong here
Personal access tokens authenticate individual users but do not provide group-based access control on experiments. They also do not govern artifact storage; artifacts would still be stored in the default workspace location. This approach does not integrate with Unity Catalog's permission model and fails to meet the governance requirements.
- ✗
Store experiment artifacts in a Unity Catalog volume and use workspace ACLs on the notebook
Why it's wrong here
Workspace ACLs on notebooks control who can view or edit the notebook, not who can access the MLflow experiment or its artifacts. While storing artifacts in a volume is good, without registering the experiment in Unity Catalog, there is no fine-grained access control on the experiment itself. This leaves experiment results accessible to anyone with workspace access.
- ✗
Enable inference tables on the MLflow experiment to log access
Why it's wrong here
Inference tables are a feature of Model Serving endpoints, not MLflow experiments. They cannot be enabled on an experiment. This option is technically invalid and does not address access control or governed storage for MLflow experiments.
- ✓
Register the MLflow experiment in Unity Catalog and grant appropriate privileges on the experiment object
Why this is correct
Unity Catalog supports registering MLflow experiments as securable objects. Once registered, you can grant privileges like USE SCHEMA and SELECT on the experiment to control access. Artifacts are stored in a Unity Catalog volume or external location, providing governance and auditability. This directly meets the requirements for access control and governed storage.
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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.