AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Predicting whether a customer responds to a particular advertising campaign is best described as a classification problem because it involves categorizing data into distinct classes or groups based on specific criteria. In this scenario, there are two possible outcomes: the customer either responds positively to the campaign or does not respond. The goal is to classify customers into these two categories based on their characteristics and behaviors.

Classification problems typically use algorithms that can model the relationship between input features (such as customer demographics, previous interactions, or purchase history) and output labels (response or no response). Approaches might include decision trees, support vector machines, or logistic regression, all aimed at determining which class a new observation belongs to.

The other options are less suitable for this scenario. Regression problems are used when the target variable is continuous, rather than discrete classifications. Clustering problems, on the other hand, involve grouping similar data points together without predefined labels, which does not apply when the focus is on predicting a specific response. Anomaly detection deals with identifying outliers or rare events, which is not the primary objective in predicting responses to an advertising campaign. Thus, classification is the most accurate terminology for this type of problem.