Q78 — AWS AIF-C01 Ch.2

Question 78 of 100 | ← Chapter 2

A company wants to create an ML solution to help sales representatives decide whether to prepare a sales proposal for a specific customer. The model will consider input data about the customer and proposal specifications. The output will be either "Approved" or "Rejected" for each sales proposal. The company already has historical labeled records. Which ML technique satisfies these requirements?

• A. Classification ✓
• B. Regression
• C. Clustering
• D. Dimensionality reduction

Correct Answer: A. Classification

Explanation

Analysis: A. Classification is a supervised learning task that assigns inputs to discrete classes or labels. Here, inputs (customer and proposal features) must be mapped to one of two discrete outcomes—"Approved" or "Rejected"—which is precisely the goal of classification. Algorithms such as logistic regression, decision trees, or random forests are applicable. B. Regression predicts continuous numeric outputs (e.g., house price, sales volume). Since the required output is discrete ("Approved"/"Rejected"), regression is inappropriate. C. Clustering is an unsupervised technique grouping similar data points into clusters. This problem requires learning a mapping from features to a known target label—not clustering. D. Dimensionality reduction reduces feature count (e.g., via PCA) for visualization or computational efficiency—but does not solve the core prediction task. Given the availability of historical labeled data, supervised classification is the appropriate technique to map customer and proposal features to binary approval decisions. Thus, classification best fulfills this business requirement.