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 inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?

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.








 

Suggested Answer: A

By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset removing target variable column is connected to the same port as the output of the Web Service Intput module.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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