A. The cost formula in a neural network, also known as the loss or objective function, measures the difference between predicted outputs and actual target values during training. It quantifies the network's performance and aids in adjusting the model's parameters to minimize errors. Common examples include mean squared error for regression tasks and cross-entropy for classification tasks.