Databricks-ML-Pro ML Ops Practice Question
Exhibit
{
"model_name": "revenue_forecast",
"version": "5",
"stage": "Staging",
"tags": {
"validation_status": "pending",
"team": "finance"
}
}Refer to the exhibit. An MLOps engineer is reviewing a JSON object representing a model in the Databricks Model Registry. The engineer wants to promote this model to the 'Production' stage using the MLflow Python API. Which command is correct?
⚠ Common exam trap
Candidates often guess the API method name, confusing it with generic MLflow logging methods or incorrectly assuming they need to delete and re-register the model to change its stage.
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
✓
client.transition_model_version_stage(name='revenue_forecast', version=5, stage='Production')
To transition a model version in MLflow, the `transition_model_version_stage` function is the standard method. It requires the model name, the specific version, and the target stage string. This API call is critical for programmatic CI/CD pipelines, allowing engineers to automate the promotion process based on validation results, thereby reducing manual effort and ensuring consistent deployment procedures across the organization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
client.transition_model_version_stage(name='revenue_forecast', version=5, stage='Production')
Why this is correct
This method is the correct MLflow API call for changing the stage of a registered model. By specifying the model name, version, and target stage, the engineer programmatically moves the model through the lifecycle, which is a fundamental requirement for automated deployment pipelines in Databricks.
- ✗
client.update_model_version(name='revenue_forecast', version=5, new_stage='Production')
Why it's wrong here
The `update_model_version` method is intended for modifying metadata like descriptions or tags, not for lifecycle stage transitions. Using the incorrect method will result in an AttributeError or a failure to update the stage, as the API structure is designed to enforce specific transition workflows.
- ✗
mlflow.register_model(model_uri='revenue_forecast/5', stage='Production')
Why it's wrong here
The `register_model` function is used for creating a new version of a model, not for moving an existing version between stages. Attempting to use this for promotion will incorrectly create a duplicate entry or fail because the model version already exists in the registry.
- ✗
client.set_model_stage(model='revenue_forecast', ver=5, to='Production')
Why it's wrong here
The method `set_model_stage` does not exist in the MLflow client library. The library uses `transition_model_version_stage` to handle these operations. Using non-existent methods will cause the script to crash, highlighting the importance of knowing the specific SDK signatures for Databricks ML development.
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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.