Databricks-ML-Pro ML Ops Practice Question
Which TWO actions are required to properly implement MLflow Model Registry stages and governance for a machine learning project?
⚠ Common exam trap
Candidates frequently select 'registering models' as the governance action, but registration is a development task. Governance specifically requires stage transitions and ACLs to enforce separation of duties.
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
✓
Transition model versions between stages like 'Staging' and 'Production'.
Implementing Model Registry stages ensures that only validated models move from development to production. By controlling access permissions, organizations enforce a separation of duties, ensuring that data scientists can register models, while only authorized engineers can promote them. This gated workflow is foundational for regulatory compliance and auditability in enterprise MLOps, preventing unauthorized model deployments into downstream production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Assign the 'Can Manage' permission on the Model Registry to all users.
Why it's wrong here
Granting 'Can Manage' permissions to all users violates the principle of least privilege. In a production environment, this would allow unauthorized users to modify model versions, change stages, or delete models, creating significant security vulnerabilities and compliance risks for the organization.
- ✓
Transition model versions between stages like 'Staging' and 'Production'.
Why this is correct
Using stages helps manage the lifecycle of a model effectively. It provides a clear signal to downstream applications about which model versions are approved for specific environments, facilitating a clean handoff from development to production and allowing for programmatic automated deployment pipelines.
- ✓
Apply ACLs to specific models to restrict access and promote actions.
Why this is correct
Access Control Lists (ACLs) are critical for securing registered models. They ensure that only designated automated service principals or individuals can transition models to 'Production', preventing accidental or malicious promotion of unvetted models into live environments, which is essential for enterprise-grade MLOps.
- ✗
Keep all model versions in the 'None' stage for maximum flexibility.
Why it's wrong here
Leaving models in the 'None' stage bypasses the lifecycle management capabilities of the registry. This makes it impossible to programmatically identify which models are intended for production, leading to confusion and increasing the likelihood of deploying experimental or broken code into production.
- ✗
Hard-code the model version string into the production application code.
Why it's wrong here
Hard-coding version strings prevents the use of dynamic stage lookups. If a model needs to be updated, the application code must be recompiled and redeployed, which creates unnecessary maintenance overhead and defeats the purpose of the registry's automated model management capabilities.
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.