By using the mlflow.log_artifact function, you can log the feature importance CSV file as an artifact within an existing MLflow run. Additionally, you can log the model using the appropriate MLflow flavor and optionally log the feature importance data as metrics for easier access and analysis. This approach ensures that all relevant information is logged and tracked within the same MLflow run.

Answer is D

mlflow does not have log_data method

Answer is D

Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_artifact

Agree!

D. mlflow.log_artifact(importance_path, "feature-importance.csv")

D. mlflow.log_artifact(importance_path, "feature-importance.csv")

D. mlflow.log_artifact(importance_path, "feature-importance.csv")