CompTIA DataX (DY0-001) — Question 9

Which of the following is a key difference between KNN and k-means machine-learning techniques?

Answer options

• A. KNN operates exclusively on continuous data, while k-means can work with both continuous and categorical data.
• B. KNN performs better with longitudinal data sets, while k-means performs better with survey data sets.
• C. KNN is used for finding centroids, while k-means is used for finding nearest neighbors.
• D. KNN is used for classification, while k-means is used for clustering.

Correct answer: D

Explanation

The correct answer is D because KNN (K-Nearest Neighbors) is primarily a classification algorithm that assigns labels to data points based on their nearest neighbors, while k-means is a clustering algorithm that groups data points into clusters based on their features. The other options incorrectly describe the functionalities and applications of each method.