AI0-001 AI Concepts and Foundations Practice Question
A data analyst needs to select two appropriate unsupervised learning techniques for clustering unlabeled data. (Choose two.)
⚠ Common exam trap
The AI0-001 exam often tests the distinction between supervised and unsupervised learning by including familiar algorithms like linear regression or decision trees as distractors, leading candidates to mistake them for clustering techniques due to their popularity in data analysis contexts.
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
✓
Hierarchical clustering
Hierarchical clustering (C) is correct because it is an unsupervised technique that groups unlabeled data by iteratively merging or splitting clusters based on a distance metric, producing a dendrogram without requiring predefined labels. K-means (E) is also correct because it is an unsupervised partitioning algorithm that assigns unlabeled points to k clusters by minimizing within-cluster variance. Linear regression (A) is a supervised method that predicts a continuous target from labeled data, so it is not a clustering technique. Support vector machine (B) is primarily a supervised classifier (and can be used for regression), not an unsupervised clustering method. Decision tree (D) is a supervised model for classification or regression on labeled data, so it does not fit the unlabeled clustering scenario.
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 target from labelled data, so it cannot group unlabelled observations into clusters. It is tempting because it is a core supervised technique for forecasting numeric values, such as predicting sales from advertising spend, where a labelled outcome exists. Here, no target variable is available.
- ✗
Support vector machine
Why it's wrong here
Support vector machines are supervised classifiers that require labelled targets, so they cannot cluster unlabeled data. The technique is tempting because it excels at margin-based classification and would be the right choice for a labelled binary or multiclass prediction task, not unsupervised grouping.
- ✓
Hierarchical clustering
Why this is correct
Hierarchical clustering builds a nested dendrogram by iteratively merging or splitting clusters, requiring no labels. It satisfies the unlabelled-data constraint, and the dendrogram lets analysts choose cluster granularity after the fact rather than fixing k in advance.
- ✗
Decision tree
Why it's wrong here
Decision trees are supervised models that split on labelled target values, so they cannot discover clusters in unlabeled data. They are tempting because they handle mixed feature types and interpretability well, and would be correct for a labelled classification or regression problem.
- ✓
K-means
Why this is correct
K-means partitions unlabeled data into k clusters by iteratively minimising within-cluster variance, directly satisfying the requirement for an unsupervised clustering technique. It operates without labelled examples, assigning each point to the nearest centroid, which makes it appropriate for the analyst's unlabelled dataset.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.