A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

• A.There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
• B.Starting a manual child run within the objective function
• C.Startinq a manual parent run before calling fmin
• D.MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
• E.Ensuring that a built-in model flavor is used for the model logging

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A Data Scientist has created a sales forecasting model, named sales-forecasting. The model is deployed to a model serving endpoint with a schema. They need to invoke this model using Python and would prefer to use a SDK function to make the request. Which method suits these requirements?

• A.Import the MLflow Deployments class and use the predict method and provide the endpoint and input parameters.
• B.Use the built in ai_query function and provide the endpoints and request parameters.
• C.Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.
• D.Make an API request to the MODEL_VERSION_URI and provide the dataframe_split as the request parameter.

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

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A Machine Learning Engineer wants to deploy a new change to their existing model training pipeline for a social media app. Currently, they use the Databricks Feature Store to create a training set to train their LightGBM model, which is then used in a Pandas UDF for batch inference to suggest potential friends to users. They have updated the feature engineering pipeline to include an additional feature function, which now computes the number of mutual friends a user has. Which test should they add to quickly inform them if something has broken in the test environment?

• A.Implement an integration test that runs the entire pipeline from feature engineering to model inference in the production environment.
• B.Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on a fake dataset.
• C.Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on live data.
• D.Implement an integration test that runs the feature engineering pipeline in the production environment.

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A Machine Learning Engineer is tasked with implementing a simple solution, requiring as little code as possible, to monitor a regression model and ensure that the R2 score does not exceed a certain threshold. If the threshold is exceeded, the solution needs to send an email to a distribution list. Which proposed solution meets the criteria?

• A.Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Alert that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
• B.Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Query that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
• C.Configure Lakehouse Monitoring using the Inference profile type. Create a Workflow that runs on a schedule and executes a SQL query that evaluates the R2 score in the profile table. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.
• D.Create a Workflow that runs on a schedule. The Workflow executes a SQL Query that calculates the R2 score. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.

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