Databricks Databricks-Machine-Learning-Professional Value Pack (Frequently Bought Together)

A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

• A. mlflow.register_model(model_uri, "best_model")
• B. mlflow.register_model(run_id, "best_model")
• C. mlflow.register_model(f"runs:/{run_id}/model")
• D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
• E. mlflow.register_model(model_uri, "model")

A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

• A.
  
• B.
  
• C.
  
• D.
  

A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

• A. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.
• B. The model signature must be provided, as Unity Catalog requires all models to have signatures.
• C. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
• D. The cluster must be configured with shared access mode instead of dedicated mode.

A Data Scientist at an online gaming company is creating a model to predict player churn. The company currently collects terabytes of player activity logs daily, which are stored in Databricks and processed for daily reporting. The Data Scientist has completed feature engineering and the resulting data is saved as a Delta Table with a size of 500GB. They need to next build the model for the most performant and cost-effective performance for Databricks. Which approach will do this?

• A. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a multi-node Databricks cluster.
• B. Load the feature data as a Spark DataFrame and train the model using SparkML's RandomForestClassifier on a multi-node Databricks cluster.
• C. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.
• D. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a single-node Databricks cluster.

Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

• A. client.log_artifact
• B. mlflow.log_figure
• C. mlflow.shap
• D. None of these operations can accomplish the task.
• E. mlflow.shap.log_explanation