Q75 — AWS AIF-C01 Ch.2

Question 75 of 100 | ← Chapter 2

A real estate company wants to build an ML model to predict residential property sale prices based on various factors—including location, size, number of bedrooms, and amenities. Which ML technique satisfies this requirement?

• A. Classification
• B. Clustering
• C. Reinforcement learning
• D. Regression ✓

Correct Answer: D. Regression

Explanation

Analysis: A. Classification assigns inputs to discrete categories (e.g., classifying images as cat or dog). However, predicting continuous sale prices is not a classification task. B. Clustering groups similar data points into clusters and is an unsupervised learning technique. Here, the goal is to learn the mapping between input features and a continuous target variable (sale price), not to cluster data. C. Reinforcement learning trains agents to maximize cumulative reward through sequential decision-making in an environment. This scenario involves no agent-environment interaction or sequential decisions. D. Regression is a supervised learning technique for predicting continuous-valued outputs. Given real estate features as input, the model must output a continuous sale price—exactly the objective of regression. Regression algorithms (e.g., linear regression, decision tree regression, random forest regression) learn the relationship between features and the target variable. Thus, regression is the most appropriate ML technique for predicting residential property sale prices from multiple features.