easyMultiple Choice
AIF-C01 Practice Question: Identify customer segments based on purchasing…
A company wants to identify customer segments based on purchasing behavior. They have unlabeled transaction data and do not know the segment definitions beforehand. Which type of machine learning should they use?
⚠ Common exam trap
AWS often tests the distinction between supervised and unsupervised learning by presenting unlabeled data scenarios, where candidates mistakenly choose supervised learning because they confuse 'identifying segments' with 'predicting a known label.'
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Unsupervised learning
Unsupervised learning is the correct choice because the company has unlabeled transaction data and no predefined segment definitions. This type of machine learning discovers hidden patterns, groupings, or structures in data without requiring labeled outputs, making it ideal for customer segmentation tasks like clustering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Semi-supervised learning
Why it's wrong here
Semi-supervised learning still requires some labelled examples to propagate labels from, and none exist here. It is the right choice when a small labelled set accompanies a large unlabelled one, such as classifying documents where only a handful carry confirmed categories.
- ✓
Unsupervised learning
Why this is correct
Unsupervised learning finds structure in unlabelled data without predefined outputs, so clustering algorithms can group transactions by purchasing behaviour and reveal segments. This satisfies the stem's constraints: no labels exist and segment definitions are unknown beforehand.
- ✗
Supervised learning
Why it's wrong here
Supervised learning needs labelled examples defining each segment, and the stem states definitions are unknown beforehand. It is correct when historical transactions already carry segment labels, letting a classifier learn the mapping rather than discovering structure itself.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning learns a policy through reward feedback from sequential actions, but segment discovery has no environment, actions or reward signal to optimise. It suits problems such as dynamic pricing or robot control, not partitioning static transaction records.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.