Q69 — AWS AIF-C01 Ch.2

Question 69 of 100 | ← Chapter 2

A company wants to analyze customer purchase data. The company aims to group customers to improve targeted marketing campaigns and intends to use an unlabeled dataset. Which ML technique satisfies these requirements?

• A. Unsupervised Learning ✓
• B. Fine-tuning
• C. Supervised Learning
• D. Transfer Learning

Correct Answer: A. Unsupervised Learning

Explanation

Unsupervised learning (Option A) is appropriate because it identifies patterns and structures in data without requiring labeled outcomes. Customer segmentation—grouping users based on purchase behavior—relies on clustering (e.g., K-means, DBSCAN) or dimensionality reduction techniques, all core unsupervised methods. Since the dataset is unlabeled and the goal is exploratory grouping for marketing optimization, supervised learning (C), fine-tuning (B), and transfer learning (D) are inapplicable—they all require labeled data or pre-trained models with task-specific adaptation.