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

Data augmentation achieves the artificial increase of dataset size by modifying existing data. This technique is particularly important in machine learning and deep learning, especially in fields such as computer vision and natural language processing, where large amounts of labeled data are often required to build effective models. By applying various transformations, such as rotation, scaling, cropping, and adding noise to images, or by altering sentences through synonym replacement and back-translation in textual data, the existing data can be varied enough to enhance the model’s ability to generalize well to unseen data.

This approach not only increases the quantity of training examples but also promotes the diversity of those examples, reducing the risk of overfitting and improving the robustness of the model. As a result, models trained on augmented data are typically more accurate and reliable when making predictions on new, real-world data, making data augmentation a vital strategy in developing effective AI solutions.