AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The defining characteristic of unsupervised learning algorithms, such as k-means, is that they group data without the need for labeled outcomes. In unsupervised learning, the algorithm is presented with input data that does not come with corresponding output labels. The main goal is to uncover patterns, structures, or groupings within the data based solely on the inherent characteristics of the data points themselves.

For k-means specifically, the algorithm works by partitioning the dataset into k distinct clusters based on feature similarities. It does this by iteratively assigning each data point to the nearest cluster center and updating the cluster centers based on the mean of the assigned data points until convergence is achieved. This process highlights the ability of unsupervised learning to discover patterns and relationships within the data without any external guidance or predefined outcomes, making option C the correct choice.

In contrast, supervised learning approaches rely on labeled data, where each input has a corresponding known output. This is not the case in unsupervised learning, emphasizing the distinct nature of how k-means operates.