Databricks-ML-Assoc Databricks Machine Learning Practice Question
Which Databricks ML component is best suited for managing access control for machine learning experiments and models across different teams?
⚠ Common exam trap
Test-takers often choose workspace-level access control lists (ACLs) or notebook permissions instead of Unity Catalog when asked about comprehensive governance across teams.
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.
Unity Catalog is the centralized governance layer in Databricks. It allows administrators to define fine-grained access controls for experiments and models, ensuring that sensitive IP is protected and that users only have access to what they need. Implementing Unity Catalog is a best practice for security and compliance, ensuring that model assets are governed as strictly as the underlying data they rely on.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Notebook tags.
Why it's wrong here
Notebook tags are for organizing and searching for notebooks, not for enforcing security or access control. Relying on tags for security would be ineffective, as they provide no actual enforcement mechanism. Access control must be handled at the platform level using dedicated identity and governance features like Unity Catalog.
- ✓
Unity Catalog.
Why this is correct
Unity Catalog provides a unified, centralized governance solution for data and machine learning assets. It enables administrators to manage permissions and access control for experiments and models, ensuring security and compliance across the workspace. It is the definitive standard for access governance in the modern Databricks lakehouse architecture.
- ✗
Environment variables in the cluster config.
Why it's wrong here
Environment variables are for configuration and runtime settings, not for access control. They do not have the capability to restrict or grant permissions to users or groups. Using them for security is incorrect and could expose sensitive information, as they are not designed to be a secure access control mechanism.
- ✗
The 'git commit' history.
Why it's wrong here
Git logs track code changes, not access to ML assets. While useful for auditing who changed what in the codebase, it does not prevent unauthorized access to experiments or model registry items. Access control must be handled by identity management systems, not by tracking version control history in external repositories.
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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.