AI-900: Select Columns in a Dataset for Training Pipeline for Regression Model

Learn how to use Select Columns in a Dataset module to select specific columns in a training pipeline for a regression model. Pass the AI-900 certification exam with ease.

Question

You are creating a training pipeline for a regression model and your dataset contains hundreds of columns. For a particular part of your model, you want to use data only from some specific columns. Which module should you add to the pipeline?

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

Answer

B. Select columns in a dataset

Explanation

This module is used to choose a subset of columns to use in downstream operations.

To use data only from specific columns in a training pipeline for a regression model, you should add the Select Columns in a Dataset module to the pipeline. This module allows you to select the columns you want to use in your model and exclude the rest. You can also rename columns, drop columns, or keep only the columns you want to use.

The Normalize Data module is used to normalize the data so that the values are all on a similar scale based relative to the minimum and maximum values in each column. The Clean Missing Data module is used to clean missing data by replacing missing values with a default value or by removing the rows or columns that contain missing values.

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.