" Overfitting " occurs when a complex model, such as a deep neural network, learns the " noise " and specific details of the training data rather than the general underlying patterns. A clear indicator of overfitting is a large gap between training performance and validation/test performance. According to the AAIA™ manual, this makes the model brittle and unable to generalize to new, unseen data in a production environment. To mitigate this, auditors should recommend techniques such as regularization, dropout, or simplifying the model architecture. Underfitting (Option C) would result in poor performance across both datasets.