Databricks-ML-Pro ML Ops Practice Question
Your organization is implementing an MLOps strategy that requires strict model governance. You need to ensure that no model is deployed to production unless it has been tagged with 'validated=true' in the MLflow Model Registry. How can you enforce this policy within your CI/CD workflow?
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
✓
Add a validation script in the CI/CD pipeline that queries the model version tags.
Implementing a policy-based deployment gate in your CI/CD pipeline ensures that governance requirements are met before code or models hit production. By querying the model's tags using the MLflow client before attempting a deployment, you create a hard stop for non-compliant models. This is a critical security and compliance practice, preventing unauthorized or untested models from being served to end-users or critical business processes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Databricks workspace permissions to prevent unauthorized users from deploying models.
Why it's wrong here
While workspace permissions are important for security, they do not enforce specific model-level metadata requirements. Even authorized users could accidentally deploy a non-validated model. You need an automated script that checks for the existence of the specific 'validated=true' tag before allowing the deployment to proceed.
- ✓
Add a validation script in the CI/CD pipeline that queries the model version tags.
Why this is correct
A custom validation script acts as a gatekeeper. By using 'mlflow.tracking.MlflowClient()' to retrieve the model version and inspect its dictionary of tags, the pipeline can verify the 'validated=true' condition. If the condition is not met, the pipeline fails, preventing the deployment from occurring.
- ✗
Rely on the MLflow UI to visually verify the tag before clicking 'Deploy'.
Why it's wrong here
Manual verification is prone to human error and does not scale in a CI/CD environment. Automated MLOps requires programmatic verification to ensure that every deployment follows the established governance policies without relying on a user remembering to check the UI every single time a model is promoted.
- ✗
Set the default stage of all new models to 'Production' via a global config.
Why it's wrong here
This approach is dangerous because it would automatically promote all experimental models to production, bypassing all validation. Production deployment should always be an explicit, gated action that confirms the model has passed all necessary quality, security, and governance checks before becoming available to production consumers.
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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.