- A
Linear regression
Why wrong: Linear regression is a supervised learning algorithm for regression, not clustering.
- B
Support vector machine
Why wrong: SVM is a supervised learning algorithm for classification, not unsupervised clustering.
- C
Hierarchical clustering
Hierarchical clustering is an unsupervised algorithm that builds a hierarchy of clusters.
- D
Decision tree
Why wrong: Decision trees are supervised learning models for classification or regression, not unsupervised.
- E
K-means
K-means is a popular unsupervised clustering algorithm.
Question 76 of 1,000
Unsupervised Learning Clustering Techniques
This AI0-001 practice question tests your understanding of ai concepts and foundations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data analyst needs to select two appropriate unsupervised learning techniques for clustering unlabeled data. (Choose two.)
Quick Answer
The answer is K-means and hierarchical clustering. These two techniques are correct because they are both unsupervised learning clustering techniques that group unlabeled data based on inherent patterns or distances, requiring no pre-existing labels or target variables. K-means partitions data into a predefined number of clusters by minimizing within-cluster variance, while hierarchical clustering builds a tree of nested clusters, either agglomeratively or divisively, without needing to specify the number of clusters upfront. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish unsupervised methods from supervised ones; a common trap is confusing K-means with a classification algorithm like logistic regression, but remember that clustering never uses labeled outputs. A useful memory tip is to think of the "K" in K-means as standing for "K-number of groups you choose," and for hierarchical clustering, picture a family tree of data points merging together.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Hierarchical clustering uses either agglomerative (bottom-up) or divisive (top-down) approaches, with linkage criteria (e.g., Ward's method, complete linkage) determining cluster merging based on distance metrics like Euclidean or Manhattan. In real-world scenarios, it is valuable for gene expression analysis where the dendrogram reveals biological hierarchies without assuming spherical clusters, unlike K-means which requires specifying K and is sensitive to initial centroids.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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