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Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
Clustering is a form of unsupervised learning, which groups data points based on similarities without predefined labels. According to ISTQB CT-AI Syllabus, clustering is used in scenarios where:
The objective is to find natural groupings in data.
The dataset does not have labeled outputs.
Patterns and structures need to be identified automatically.
Analyzing the answer choices:
A . Associating shoppers with their shopping tendencies Correct
Shoppers can be grouped based on purchasing behaviors (e.g., luxury shoppers vs. budget-conscious shoppers), which is a typical clustering application in market segmentation.
B . Grouping individual fish together based on their types of fins Incorrect
If the types of fins are labeled, it becomes a classification problem, which requires supervised learning.
C . Classifying muffin purchases based on packaging attractiveness Incorrect
Classification, not clustering, because attractiveness scores or labels must be predefined.
D . Estimating the expected purchase of cat food after an ad campaign Incorrect
This is a prediction task, best suited for regression models, which are part of supervised learning.
Thus, Option A is the best answer, as clustering is used to group shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).