Answer-first summary for fast verification
Answer: mlflow.set_model_version_stage
The correct answer is **C. mlflow.set_model_version_stage**. Here's why: - **mlflow.transition_model_version**: A generic function for transitioning a model version between stages, requiring the target stage as an argument, but not specific to 'Production'. - **mlflow.deploy_model_version**: Deploys a model version to a specific environment, inferring the stage based on environment configuration, not directly setting the stage. - **mlflow.set_model_version_stage**: Explicitly sets the stage of a model version to the specified value, making it the most direct method for this purpose. - **mlflow.update_model_version_stage**: Not a valid MLflow operation; no such function exists for updating a model version's stage. Thus, to transition the model version to the 'Production' stage in the MLflow Model Registry, the engineer should use `mlflow.set_model_version_stage('Production')`.
Author: LeetQuiz Editorial Team
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When deploying a model using MLflow in a Databricks environment and aiming to serve the model as a REST API endpoint via the MLflow Model Registry, which operation is most appropriate for transitioning the model version to the 'Production' stage?
A
mlflow.deploy_model_version
B
mlflow.update_model_version_stage
C
mlflow.set_model_version_stage
D
mlflow.transition_model_version
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