DP-100 practice question 18 of 35

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

Domain Deploy and operationalize machine learning solutions

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?

Answer options

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. Correct answer

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.

Correct Answer:

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.

Explanation

When a real-time inference pipeline is generated from a training pipeline in Azure Machine Learning designer, the Web Service Input module schema is based on the original training dataset, which includes the label column. The requirement is that the deployed service should not expect the label as an input. The standard, documented procedure to resolve this is to modify the inference pipeline by inserting a Select Columns in Dataset module immediately after the Web Service Input module. This module is then configured to exclude the label column, ensuring that only the feature columns are passed to the Score Model module for prediction. This correctly adjusts the data schema within the pipeline for inference.

Why Incorrect

B: Deleting the dataset from the training pipeline is incorrect. The dataset is required for training, and the task is to modify the already-generated inference pipeline. C: The Web Service Input module is the entry point for data in the deployed endpoint. Deleting it would make the inference pipeline non-functional. D: The inference pipeline is generated automatically with a Web Service Input module. The standard practice is not to replace this module but to modify the data stream that flows from it.

References

1. Microsoft Learn Documentation. In the official tutorial for deploying a designer model

the exact scenario is addressed. After creating the real-time inference pipeline

the documentation explicitly instructs the user to "Remove the label column... using the Select Columns in Dataset module" and place it between the Web Service Input and Score Model modules.

Source: Microsoft

"Tutorial: Deploy a machine learning model with the designer

" Azure Machine Learning Documentation.

Reference: See the section "Create a real-time inference pipeline

" Step 4.

2. Microsoft Learn Documentation. The reference for the Select Columns in Dataset module describes its function as choosing a subset of columns for downstream operations

which is precisely its use case in this scenario—to filter out the label column before scoring.

Source: Microsoft

"Select Columns in Dataset component

" Azure Machine Learning Component Reference.

Reference: See the "Overview" section.

Keep practicing

Take the free DP-100 practice test

Ten exam-style questions with answers and explanations, plus the exam facts and study guides.

Start the free practice test