A Machine Learning Engineer wants to monitor the quality and stability of their machine learning model's predictions over time. They have a Delta table, retail_inference_log, which records each model prediction along with input features, a timestamp, and (when available) the true label. They need to detect data drift and monitor model performance trends using Databricks Lakehouse Monitoring, ensuring that alerts are triggered if the distribution of predictions or input features changes significantly. Which approach will set up monitoring for this use case?

• A. Create a monitor with the Inference profile on the retail_inference_log table, specifying the timestamp column and the columns for model inputs, predictions, and labels. Configure the monitor to compute drift and performance metrics over time windows.
• B. Create a monitor with the Snapshot profile on the retail_inference_log table, so that metrics are calculated over the entire table each time the monitor runs and therefore is able to compare new values with previous ones to compute data drift.
• C. Create a monitor with the Inference profile on the retail_inference_log table, and specify a recent batch of production data as the baseline table for drift detection. Use this recent production data to compare against new data for drift and performance monitoring.
• D. Create a monitor with the Time Series profile on the retail_inference_log table, specifying the timestamp column and including model input, prediction columns and the true label column. This will track drift in features and predictions over time, and model performance could also be tracked using a custom metric.

A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

• A. df = fs.get_missing_features(spark_df, model_uri)
  fs.score_batch(model_uri, df)
• B. df = fs.get_missing_features(spark_df)
  fs.score_batch(model_uri, df)
• C. fs.score_batch(model_uri, spark_df)
• D. fs.score_model(model_uri, spark_df)
• E. df = fs.get_missing_features(spark_df, model_uri)
  fs.score_model(model_uri, df)

A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

• A. Replace POST with GET in the call to http request
• B. Replace list with webhooks in the endpoint URL
• C. Replace list with view in the endpoint URL
• D. Replace POST with PUT in the call to http request
• E. There are no necessary changes

A Data Scientist is developing a model training pipeline on Databricks and needs to track custom performance metrics during training. They want to log a custom evaluation score (team_score), a single hyperparameter, and a confusion matrix plot as part of their MLflow experiment. Which code snippet correctly logs all three types of information in MLflow?

• A. mlflow.log_metric({"team_score": 0.92})
  mlflow.log_param(["max_depth", 5])
  mlflow.log_file("confusion_matrix.png")
• B. mlflow.log_param("max_depth", 5)
  mlflow.log_metric("team_score", 0.92)
  mlflow.log_artifact("confusion_matrix.png")
• C. mlflow.log_param("max_depth", 5)
  mlflow.log_metric("team_score", 0.92)
  mlflow.log_artifact(open("confusion_matrix.png"))
• D. mlflow.log_param("max_depth", "5")
  mlflow.log_metric("team_score", "0.92")
  mlflow.log_artifact("confusion_matrix.png")