Microsoft Azure AI Fundamentals (AI-900) Practice Exam

To predict the animal population of an area, which type of Azure Machine Learning should you use?

1. Classification

2. Regression

3. Clustering

4. Anomaly detection

The correct answer is: Regression

To predict the animal population of an area, regression is the appropriate choice because this method is specifically designed to deal with continuous numerical outcomes. In this scenario, the goal is to forecast a specific quantity, which in this case is the number of animals in a given area. Regression techniques can handle various types of data relationships and can provide an estimate of population size based on factors such as environmental conditions, food availability, and previous population data. Regression models analyze historical data to identify patterns that can be used for prediction. For instance, if you have data on past animal populations alongside variables that may influence these populations, regression allows you to understand how these factors relate to the total number of animals, enabling you to produce numerical population estimates. The other options, such as classification, clustering, and anomaly detection, serve different analytical purposes. Classification is used for predicting categorical outcomes, clustering is about grouping data points based on similarity, and anomaly detection focuses on identifying outliers or abnormal patterns in the data. Since predicting a population involves estimating a numeric value rather than categorizing or grouping data, regression is the most suitable choice.