AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

A support vector machine (SVM) is indeed a supervised learning model that is predominantly utilized for classification tasks, but it can also be adapted for regression. The fundamental idea behind SVM is to find the optimal hyperplane that separates different classes in the feature space with the maximum margin. This margin is defined as the distance between the hyperplane and the nearest data points from either class, known as support vectors.

In addition to its core function in classification, SVM can also be employed in regression tasks through Support Vector Regression (SVR), allowing it to predict continuous outcomes while ensuring robust generalization by minimizing error. This combination of versatility in handling both classification and regression problems is a distinguishing feature of SVM.

The other options reflect distinct learning paradigms that do not align with the defining traits of SVM. Unsupervised learning involves identifying patterns in data without labeled outcomes, clustering data focuses specifically on grouping similar instances without reference to known categories, and reinforcement learning emphasizes decision-making through interactions with an environment to maximize cumulative rewards.