Databricks-ML-Assoc ML Workflows Practice Question
Exhibit
{
"model_name": "revenue_forecast",
"run_id": "a1b2c3d4e5f6g7h8",
"stage": "Staging",
"status": "READY",
"tags": {
"team": "finance",
"project": "q3_forecast"
}
}Refer to the exhibit. A machine learning engineer wants to promote this model to Production. Which MLflow action should be performed to achieve this while ensuring the model is ready for deployment?
⚠ Common exam trap
Candidates often confuse manual model logging with registry stage transitions, incorrectly selecting options related to saving files or creating new experiments instead of using the specific API for stage promotion.
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
✓
Call the transition_model_version_stage API to move the model to Production.
The MLflow Model Registry provides the `transition_model_version_stage` function to promote models between stages like Staging and Production. Promoting a model involves updating its state in the registry, which then allows downstream deployment pipelines to identify the correct model version. This structured approach ensures that only verified models are promoted to production environments, maintaining governance and stability in the model deployment process.
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 the mlflow.register_model() function with the production flag.
Why it's wrong here
The register_model function is used to initially add a model to the registry from a specific training run. It is not the correct command for moving an existing model version from the 'Staging' stage to the 'Production' stage, which requires the transition_model_version_stage operation.
- ✓
Call the transition_model_version_stage API to move the model to Production.
Why this is correct
The transition_model_version_stage API is the standard method for updating the stage of a registered model version. By programmatically changing the stage to 'Production', the model becomes available for production deployment services to fetch and serve the most current approved model artifact for live traffic.
- ✗
Delete the model from Staging and re-upload it to the Production path.
Why it's wrong here
Deleting and re-registering models destroys the lineage and audit trail associated with the original run. The Model Registry is designed to manage lifecycle states through transitions, and manual deletion bypasses the built-in tracking mechanisms that record when and why a model was moved to production.
- ✗
Update the tag 'status' to 'Production' in the model metadata.
Why it's wrong here
Updating a tag is purely informational and does not change the programmatic stage of the model in the registry. Deployment tools and API filters specifically look at the 'stage' attribute of the model version, not custom tags, to determine which model should be used for serving.
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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.