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AIF-C01 Practice Question: A team needs to identify customer segments based…
A team needs to identify customer segments based on purchasing behavior without predefined categories. Which algorithm should they use?
⚠ Common exam trap
AWS often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may confuse clustering (unsupervised) with classification (supervised) algorithms like logistic regression or decision trees when the question explicitly states 'without predefined categories'.
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
✓
K-means clustering
K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on feature similarity without requiring predefined labels or categories. Since the team needs to identify customer segments solely from purchasing behavior data, K-means is the appropriate choice as it discovers natural groupings in the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric target from labelled data, so it cannot assign observations to discovered groups. It would be the right choice for forecasting a value such as expected spend, not for unsupervised segmentation.
- ✗
Decision tree
Why it's wrong here
A decision tree requires labelled target classes to split on, so it cannot discover segments when no predefined categories exist. It is tempting because decision trees excel at supervised classification and regression with known outcomes, such as predicting churn from historical labels — but clustering, not classification, suits unlabelled behavioural grouping.
- ✓
K-means clustering
Why this is correct
K-means clustering partitions unlabelled data into k groups by minimising within-cluster variance, so it discovers purchasing-behaviour segments without predefined categories. This satisfies the stem's unsupervised requirement, unlike supervised classifiers that need labelled examples. It outputs cluster assignments directly, making it the appropriate choice for exploratory customer segmentation.
- ✗
Logistic regression
Why it's wrong here
Logistic regression predicts a binary or categorical label from labelled training data, so it cannot discover latent groupings when no target variable exists. It is tempting because it classifies outcomes effectively, and would be the right choice for predicting churn or purchase likelihood once segments are already defined.
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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.