Databricks-ML-Pro Model Deployment Practice Question
An ML engineer is transitioning a model from the Workspace Model Registry to the Unity Catalog (UC) Model Registry. Which TWO statements describe benefits or requirements of using Unity Catalog for model management? (Select TWO)
⚠ Common exam trap
Candidates often confuse Unity Catalog with legacy workspace registry features, failing to recognize that UC's main value is cross-workspace sharing and the three-level namespace structure.
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 supports model sharing across multiple Databricks workspaces.
Unity Catalog centralizes governance for all data and AI assets, providing a unified interface for access control and lineage. Transitioning to UC enables fine-grained permissions and allows models to be shared across different workspaces within the same Metastore. This is a significant improvement over the legacy workspace-specific registry which lacked cross-workspace visibility and unified auditing.
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 supports model sharing across multiple Databricks workspaces.
Why this is correct
By utilizing a centralized Metastore, Unity Catalog allows models to be registered once and accessed by authorized users across any workspace linked to that Metastore. This facilitates better collaboration and standardization across large organizations that use separate environments for development, testing, and production stages.
- ✗
Models must be stored in a legacy DBFS location to be registered in Unity Catalog.
Why it's wrong here
Unity Catalog requires models to be stored in UC-managed or external volumes rather than the legacy DBFS root. Using legacy DBFS would bypass the governance and security controls that Unity Catalog is designed to provide, making it incompatible with the modern governance framework required for enterprise-level deployments.
- ✓
Unity Catalog models use a three-level namespace: catalog, schema, and model name.
Why this is correct
The three-level namespace provides a structured way to organize and discover assets. This hierarchy ensures that model names only need to be unique within a specific schema, allowing different teams to manage their own namespaces without naming conflicts, while providing a clear path for data lineage and governance.
- ✗
The legacy MLflow Stages (Staging, Production) are the primary way to manage UC models.
Why it's wrong here
Unity Catalog replaces the concept of fixed Stages with more flexible Model Aliases and Tags. Aliases allow engineers to assign custom labels like 'Champion' or 'Challenger' to specific versions, providing a more robust and descriptive way to manage deployment workflows compared to the rigid legacy stage-based system.
- ✗
Only models logged with the SparkML flavor can be registered in the Unity Catalog.
Why it's wrong here
Unity Catalog is flavor-agnostic and supports any MLflow-compatible model, including Scikit-Learn, PyTorch, and XGBoost. Restricting the registry to SparkML would severely limit its utility for modern data science teams who use a wide variety of libraries to build and deploy their machine learning solutions.
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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.