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AIF-C01 Practice Question 1: Unlabeled Customer Segmentation

AWS Certified AI Practitioner (AIF-C01) · Test 1 · question 1 of 10

Fundamentals of AI and MLeasy

A retailer has two years of purchase history for its customers but no labels describing customer types. The marketing team wants the data itself to reveal groups of customers with similar buying behavior so they can design targeted campaigns. Which type of machine learning fits this goal?

Select one answer
Show answer and explanation

Answer:

  • B. Unsupervised learning, clustering customers by similarity in their purchase data

Unsupervised learning finds structure in data that has no labels. Clustering groups records that look alike, which is exactly "let the data reveal customer segments" (option B). The team doesn't know the segments in advance, so there's nothing to train a model to predict.

- A is wrong because a classifier is supervised. It needs historical examples that are already labeled with the correct segment, and this retailer has none. - C is wrong because reinforcement learning trains an agent to choose actions that maximize a reward through trial and error. It doesn't discover groups in a static dataset. - D is wrong because regression predicts a known numeric target, here spend. That's a different question from which natural groups exist, and it's still supervised.

Reference: What's the Difference Between Supervised and Unsupervised Learning?

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