AI-900: How to handle missing data in Azure

Learn how to use the Clean missing data module in Azure Machine Learning to handle missing values in your dataset for the AI-900 certification exam.

Question

You are creating a training pipeline for a regression model and you want to make sure that the dataset is complete, otherwise you need to perform various operations to fix the data. Which module should you add to the pipeline?

A. Select columns in a dataset
B. Clean missing data
C. Normalize data

Answer

B. Clean missing data

Explanation

Clean missing data helps to check data for missing values and then perform various operations to fix the data or insert new values. The goal of such cleaning operations is to prevent problems caused by missing data that can arise when training a model.

The correct answer is B. Clean missing data. This module allows you to handle missing values in your dataset by either removing them or replacing them with a specified value. Missing values can affect the performance and accuracy of your regression model, so it is important to check and fix them before training. The other two modules are not related to handling missing values, but rather to selecting or transforming the features in your dataset.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.