Databricks-ML-Pro ML Ops Practice Question
You are managing a Databricks environment and need to ensure that ML models are reproducible across different workspaces. Which strategy is most effective for cross-workspace model promotion?
⚠ Common exam trap
Candidates often suggest manual artifact copying or exporting/importing pickle files, failing to realize that Unity Catalog provides a centralized, secure, and native way to share models across workspaces.
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 a central MLflow Model Registry in Unity Catalog to share model versions across workspaces.
Using a centralized MLflow Model Registry via Unity Catalog allows models to be shared across workspaces securely. This avoids the manual export/import of artifacts and ensures that lineage, tags, and versioning remain consistent. By managing access through Unity Catalog permissions, you ensure that only authorized environments can read or promote specific models, creating a unified, compliant, and highly scalable model deployment lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Download the model artifact from the source workspace and upload it to the target workspace UI.
Why it's wrong here
Manual downloading and uploading is insecure, inefficient, and breaks the model lineage. It does not transfer experiment history, metadata, or associated tags, leading to a fragmented view of the model's history and potential for human error during the transfer process between different Databricks workspaces.
- ✓
Use a central MLflow Model Registry in Unity Catalog to share model versions across workspaces.
Why this is correct
Unity Catalog's centralized model registry provides a single source of truth for all models, regardless of which workspace they are accessed from. This allows teams to promote models through environments (dev, staging, prod) while maintaining full auditability, lineage, and consistent governance over the model's lifecycle.
- ✗
Export the model as a pickled Python object and email it to the operations team.
Why it's wrong here
Emailing model files is a major security risk and violates standard data governance policies. It completely bypasses the MLflow Model Registry, losing all version control, audit trails, and metadata associated with the model, making it impossible to track or reproduce in a production environment.
- ✗
Re-run the training notebook in each workspace to recreate the model artifact locally.
Why it's wrong here
Re-running training in every workspace does not guarantee the same model, as data sources or library versions might differ. This approach is inefficient, wastes compute resources, and provides no assurance that the model version deployed in production is exactly the same as the one tested in staging.
About these practice questions
One of 300 original Databricks-ML-Pro practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.