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.