Databricks Certified Machine Learning - Associate
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A machine learning engineer is enhancing a project to automatically refresh the model each time the project runs. The project is connected to an existing model named 'model_name' in the MLflow Model Registry. The following code snippet is part of their strategy: mlflow.sklearn.log_model(sk_model=model, artifact_path='model', registered_model_name=model_name). Given that 'model_name' is already present in the MLflow Model Registry, what does the parameter registered_model_name=model_name signify?_
A
It removes the need to specify the model name in a subsequent call to mlflow.register_model._
B
It creates a new model entry titled 'model_name' in the MLflow Model Registry._
C
It specifies the name of the logged model in the MLflow Experiment.
D
It adds a new version of the 'model_name' model in the MLflow Model Registry._
E
It indicates the name of the Run in the MLflow Experiment.