In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

• A.The Job ID field returned in a query using the Jobs API
• B.The Job ID field in the Jobs page in the UI
• C.The Job ID field in the Job details section of the specific Job page in the UI
• D.All of these approaches

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A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?

• A.Use collect() to gather all data and process regions using standard pandas groupby.
• B.Use groupBy("customer_region") and apply a training function with applyInPandas().
• C.Use foreachBatch() to sequentially process each region's data.
• D.Use mapInPandas() to apply the training function across all partitions without grouping.

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A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

• A.mlflow.log_metric
• B.There is no way to store values like this.
• C.mlflow.log_artifact
• D.mlflow.log_model
• E.mlflow.log_param

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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 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.
• C.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.
• 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.

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A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

• A.Measure the latency of the prediction time
• B.Obtain the observed values (actual) feature values
• C.Compute the evaluation metric using the observed and predicted values
• D.Retrain the model
• E.None of these should be completed as Step #3

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