AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The correct choice emphasizes that k-means clustering specifically requires the data to be numeric. K-means operates by calculating distances between data points to identify clusters, and these calculations necessitate numeric data. This means that any categorical data needs to be transformed into a numerical format before applying this clustering algorithm.

The requirement for clusters to be spherical in shape stems from the algorithm's reliance on the mean to define the centroid of clusters. K-means finds clusters that are effectively circular or spherical in their distribution throughout the dataset, as it minimizes the variance within each cluster.

Additionally, the k-means algorithm indeed requires the number of clusters to be specified beforehand, which is a critical parameter for its execution. It is design-focused on partitioning data into a predetermined number of clusters based on the distance between points.

In contrast, saying that data can be of various types suggests that k-means can handle different data types directly. Since k-means only deals effectively with numeric data, this assertion is incorrect as it contradicts the fundamental requirements of the algorithm. Thus, this option is not aligned with the requirements for applying k-means clustering.