Microsoft Certified Azure Data Scientist Associate - DP-100
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You create a training pipeline for a classification model in Azure Machine Learning designer. This pipeline uses a dataset containing both features and labels. After creating a real-time inference pipeline from this training pipeline, you observe that the web service input schema includes the label column that the model is designed to predict. Client applications using this web service must not be required to provide a value for this label column.
What should you do to modify the inference pipeline to meet this requirement?
A
Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.
B
Delete the dataset from the training pipeline and recreate the real-time inference pipeline.
C
Delete the Web Service Input module from the inference pipeline.
D
Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.