Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, making them particularly useful in tasks such as time series forecasting, natural language processing, and speech recognition. The architecture of RNNs includes loops, allowing information to persist. This characteristic enables RNNs to remember previous inputs and use that information to influence the output of subsequent data points in the sequence.

For example, in natural language processing, RNNs can maintain context across sentences, making them adept at understanding the relationship between words that appear in a particular order. In contrast, models like Support Vector Machines, Convolutional Neural Networks, and Random Forests are not inherently suitable for sequential data. Support Vector Machines, while effective for classification tasks, process inputs independently and do not account for sequence information. Convolutional Neural Networks are primarily designed for spatial data, such as images, and although they can be adapted for sequence data, they are not specialized for this purpose. Random Forests, on the other hand, are ensemble methods that operate by averaging predictions from multiple decision trees on independent samples, without any consideration for the order of data. Therefore, RNNs stand out as the appropriate model for tasks that involve sequences.