Databricks-ML-Pro ML Ops Practice Question
Which TWO statements regarding the use of Unity Catalog in Databricks for MLOps are correct?
⚠ Common exam trap
Candidates often assume that models registered in Unity Catalog are isolated to a single workspace, missing that cross-workspace sharing and centralized governance are core advantages of using Unity Catalog for enterprise ML.
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
✓
It enables centralized lineage tracking between data tables and MLflow models.
Unity Catalog acts as the centralized governance layer for all data and AI assets. It provides fine-grained access control and lineage tracking, allowing organizations to maintain visibility over which data is used by which models. By centralizing these assets, teams can collaborate safely while ensuring that compliance and auditing requirements are satisfied across the entire organization, regardless of the individual workspace where the work is performed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog enforces access control at the table level only, not on MLflow models.
Why it's wrong here
Unity Catalog governs MLflow models just as it governs data assets. It provides centralized permissions that apply to models, allowing administrators to control who can view, use, or manage models across the organization, providing consistent security across both data and machine learning artifacts.
- ✓
It enables centralized lineage tracking between data tables and MLflow models.
Why this is correct
Unity Catalog provides a unified lineage graph that shows how data flows from source tables into ML models. This visibility is essential for understanding the impact of data changes on model performance and for fulfilling regulatory requirements regarding data provenance and model transparency.
- ✗
Unity Catalog is only compatible with Python and does not support SQL-based model management.
Why it's wrong here
Unity Catalog is language-agnostic and provides a consistent interface for managing assets via SQL, Python, or the UI. This supports teams using different toolsets, ensuring that governance policies are applied uniformly regardless of the programming language used to interact with the models or data.
- ✓
Models registered in Unity Catalog can be shared across multiple workspaces.
Why this is correct
A key benefit of Unity Catalog is its ability to facilitate cross-workspace sharing. Models registered in the catalog can be accessed from different workspaces, which is vital for organizations that separate dev, staging, and production environments but require a single, governed registry of model assets.
- ✗
Users must manually export model artifacts to an external store to share them via Unity Catalog.
Why it's wrong here
Unity Catalog integrates directly with the MLflow model registry, so no manual export is required. The platform handles the underlying storage and access, ensuring that sharing is seamless, secure, and compliant without requiring additional manual steps that could compromise security or introduce errors.
About these practice questions
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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-Pro 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-Pro exam.