Microsoft Azure AI Fundamentals (AI-900) Practice Exam 2025 - Free AI-900 Practice Questions and Study Guide

The primary goal of a classification model is to assign labels to input data. In classification tasks, the model takes input features and then categorizes or classifies them into predefined classes or labels. This is useful in various applications such as email spam detection, where incoming messages are labeled as "spam" or "not spam," and image recognition, where images are classified into categories like "cat," "dog," or "car."

Classification models operate based on training data that has features along with their corresponding labels, allowing them to learn patterns and relationships. Once trained, the model can then predict the label for new, unseen data points based on those learned patterns, thus achieving its primary goal.

In this context, predicting continuous values relates to regression tasks rather than classification. Grouping similar items typically pertains to clustering, which involves unsupervised learning rather than assigning specific labels to known categories. Identifying anomalies is a distinct task generally handled by anomaly detection models, which focus on recognizing unusual patterns rather than classifying standard data points into defined categories.