Databricks Databricks-Machine-Learning-Professional Exam With Confidence Using Practice Dumps

The correct line of code is “MODEL_VERSION_TRANSITIONED_STAGE”, which is the name of the event that occurs when a model version transitions to any stage in the MLflow Model Registry. This event can be used to trigger a webhook that will execute a Databricks Job based on the model version and the stage it transitions to. The other options are either invalid event names or too specific for the task.

References: MLflow Model Registry Webhooks, MLflow Model Registry Events

To log feature importance data from a CSV file as an artifact of an MLflow run, the correct method is mlflow.log_artifact, which takes the local file path and the artifact path as arguments.The mlflow.log_model method is used to log a model with a specific flavor, such as pyfunc or spark. The mlflow.log_data method does not exist. The mlflow.log_model_and_data method also does not exist, and it is not necessary to log both the model and the data in this case. References:

• MLflow Tracking API
• MLflow Models

To create nested runs in MLflow, you need to specify the nested=True argument in the parent run, and omit it in the child runs. This will set a special tag (mlflow.utils.mlflow_tags.MLFLOW_PARENT_RUN_ID) in the child runs that indicates the parent run ID. This way, the UI will show the child runs under the parent run in a collapsible hierarchy. The indentation of the code blocks is not relevant, as long as the child runs are created within the context of the parent run. The run name parameter is also not relevant, as it does not affect the nesting of the runs. References: Understanding Parent and Child Runs in MLflow, Nested runs using MLflowClient, creating-child-runs-in-mlflow