Databricks-ML-Pro ML Ops Practice Question
Exhibit
{
"model_name": "revenue_forecast",
"version": 4,
"stage": "None",
"tags": {
"status": "pending_review",
"owner": "finance_team"
}
}Refer to the exhibit. You are managing the 'revenue_forecast' model in the registry. A colleague wants to deploy this version to production. Which Databricks command or process is required to move version 4 to the 'Production' stage while ensuring existing production models remain unaffected?
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 the MLflow transition_model_version_stage API with archive_existing_versions=True.
In Databricks, using the 'transition_model_version_stage' method with 'archive_existing_versions=True' is the standard practice for seamless deployments. This automatically moves the previous production version to 'Archived', maintaining a clear audit trail. Proper lifecycle management ensures that only one version is active in production at a time, preventing conflicts and ensuring that the most current, verified model is the one serving live traffic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the existing Production version and then promote version 4 to Production.
Why it's wrong here
Deleting models is bad practice because it destroys the audit history and lineage of the deployment. You should always archive older versions instead of deleting them, so that you can roll back to them if the new model encounters performance issues in the production environment.
- ✓
Use the MLflow transition_model_version_stage API with archive_existing_versions=True.
Why this is correct
This method is the programmatic way to promote a new version while safely archiving the previous one. Setting the parameter to True ensures that the registry remains clean and that there is no ambiguity regarding which model version is currently serving production traffic for the 'revenue_forecast' application.
- ✗
Directly update the 'stage' tag in the model JSON definition to 'Production'.
Why it's wrong here
Updating metadata tags does not change the internal stage status managed by the MLflow Model Registry. The stage must be updated via the specific 'transition_model_version_stage' API to properly trigger registry events and update the underlying database records associated with the model version lifecycle.
- ✗
Reregister the model as a new model name to avoid conflicts with version 3.
Why it's wrong here
Creating a new model name is unnecessary and breaks the lineage of the 'revenue_forecast' model. The Model Registry is specifically designed to handle multiple versions of the same model. Reregistering causes confusion in downstream applications that are configured to point to the original model name.
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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.