Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data science team is transitioning from local model development to Databricks. They want to ensure their models are portable across different Databricks workspaces. What is the recommended practice for managing models in this environment?
⚠ Common exam trap
Candidates often suggest manual model exporting or cloud storage transfers, unaware that Unity Catalog and the Model Registry provide native, governed cross-workspace access without manual file movement.
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
✓
Use MLflow Model Registry with Unity Catalog.
Using the MLflow Model Registry within Unity Catalog is the best practice for cross-workspace model management. Unity Catalog provides a unified namespace and governance, allowing models to be registered and then accessed from different workspaces with appropriate permissions. This practice eliminates the need for manual migration of artifacts and ensures that model lineage, versions, and metadata remain synchronized and governed, which is critical for enterprise-grade MLOps and compliance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export models as pickle files to DBFS.
Why it's wrong here
Using pickle files for model persistence is insecure and prone to compatibility issues across different environments. It does not provide versioning, lifecycle management, or governance. Manual file management in DBFS also lacks the metadata tracking and lineage features provided by the MLflow Model Registry, making it unsuitable for production.
- ✓
Use MLflow Model Registry with Unity Catalog.
Why this is correct
Unity Catalog provides a centralized, governed repository for models across all Databricks workspaces. It enables teams to share models securely and maintain a single source of truth. This approach supports standardized deployment pipelines and metadata management, ensuring models are easily accessible and managed throughout their lifecycle in multi-workspace environments.
- ✗
Email model artifacts to other team members.
Why it's wrong here
Emailing model files is highly insecure and violates standard MLOps practices. It lacks any version control, lineage, or authorization, making it impossible to audit or manage the model effectively. This practice introduces significant operational risk and is entirely incompatible with a professional Databricks machine learning workflow and environment.
- ✗
Re-train the model in each workspace.
Why it's wrong here
Re-training in each workspace wastes significant compute resources and risks model drift due to differences in training data or environment configurations. It also makes it difficult to maintain consistency across the organization. A centralized registry allows you to build the model once and deploy it consistently everywhere.
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-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.