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 is an unsupervised learning technique that groups unlabeled data points into a tree-like structure (dendrogram) based on similarity, without requiring predefined cluster counts. It is appropriate for clustering tasks where the data lacks labels, making it a correct choice for this question.
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 is a supervised learning algorithm for regression, not clustering.
- ✗
Support vector machine
Why it's wrong here
SVM is a supervised learning algorithm for classification, not unsupervised clustering.
- ✓
Hierarchical clustering
Why this is correct
Hierarchical clustering is an unsupervised algorithm that builds a hierarchy of clusters.
- ✗
Decision tree
Why it's wrong here
Decision trees are supervised learning models for classification or regression, not unsupervised.
- ✓
K-means
Why this is correct
K-means is a popular unsupervised clustering algorithm.
About these practice questions
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JA
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.