Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Autoencoders are a type of artificial neural network used specifically for dimensionality reduction. They operate by compressing the input data into a lower-dimensional representation in the encoding layer and then reconstructing the original data from this reduced representation in the decoding layer. This process enables the model to capture the essential features of the input data while discarding less important information, thus achieving reduction in the dimensionality of the dataset.

In contrast, clustering algorithms primarily focus on grouping similar data points into clusters, rather than reducing dimensions. Decision trees are models used for classification and regression tasks and do not inherently focus on dimensionality reduction. Support vector machines, on the other hand, are used for classification and can implicitly handle high-dimensional data without reducing dimensions in the first place. Thus, among the given options, autoencoders are uniquely suited for the task of dimensionality reduction.