You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training. You create a real-time…

Question

Options

• AAdd a Select Columns in Dataset module to the inference pipeline after the dataset and use it to
• BDelete the dataset from the training pipeline and recreate the real-time inference pipeline.
• CDelete the Web Service Input module from the inference pipeline.
• DReplace the dataset in the inference pipeline with an Enter Data Manually module that includes

• A
  71% (25)
• B
  6% (2)
• C
  17% (6)
• D
  6% (2)

Explanation

When Azure ML Designer auto-generates a real-time inference pipeline from a training pipeline, it uses the full training dataset schema-including the label column-as the web service input schema. To exclude the label column from what clients must submit, add a 'Select Columns in Dataset' module immediately after the dataset component and configure it to exclude the label column. Connect its output to the model's scoring input. This removes the label from the web service's input contract without breaking the model. Deleting the dataset or Web Service Input module would break the pipeline, and replacing the dataset with 'Enter Data Manually' is a static workaround that does not serve real client requests.

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