Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist has trained a scikit-learn model and logged it with MLflow. They now want to register the model in the Databricks Model Registry and transition it to the 'Production' stage. Which sequence of MLflow API calls should they use?
⚠ Common exam trap
The trap here is using non-existent top-level functions like mlflow.set_model_stage() instead of the MlflowClient method.
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
✓
mlflow.register_model() then mlflow.tracking.MlflowClient().transition_model_version_stage()
The correct workflow is to register the model with mlflow.register_model(), then use MlflowClient.transition_model_version_stage() to move the version to 'Production'. Other options use non-existent functions or omit the stage transition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
mlflow.register_model() then mlflow.tracking.MlflowClient().transition_model_version_stage()
Why this is correct
This sequence is correct: first register the model using mlflow.register_model(), which creates a new model version in the registry. Then use the MlflowClient's transition_model_version_stage() method to move that version to the 'Production' stage. This is the standard workflow for model lifecycle management in Databricks. The client provides methods to manage stages and transitions.
- ✗
mlflow.create_model_version() then mlflow.transition_model_stage()
Why it's wrong here
mlflow.create_model_version() is not a top-level MLflow function; model versions are created via mlflow.register_model() or the client's create_model_version() method. Also, mlflow.transition_model_stage() is not a top-level function. The correct approach uses the client. This option confuses API levels. The sequence is incorrect because it uses non-existent functions.
- ✗
mlflow.register_model() then mlflow.set_model_stage()
Why it's wrong here
mlflow.set_model_stage() does not exist. The correct method to transition a model version stage is via MlflowClient.transition_model_version_stage(). This option is a distractor because it sounds like a plausible function name, but it is not part of the MLflow API. Using it would result in an error. The registration step is correct, but the stage transition is wrong.
- ✗
mlflow.log_model() then mlflow.register_model()
Why it's wrong here
mlflow.log_model() is not a valid function; model logging is done with flavor-specific functions like mlflow.sklearn.log_model(). Also, this sequence only logs and registers, but does not transition to a stage. The question asks for transitioning to 'Production', which requires an additional step. Thus, this option is incomplete and uses an incorrect function 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-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.