A. Dropout is a regularization technique used in neural networks to prevent overfitting. During training, a random subset of neurons is "dropped out" by setting their outputs to zero with a certain probability. This forces the network to learn more robust and independent features, as it cannot rely on specific neurons. Dropout improves generalization and reduces the risk of overfitting.