AAISM Domain 2 Practice Test

In a feedforward neural network, information moves in one direction—from the input through any hidden layers to the output—with no cycles or loops. This single-pass flow means each input is processed through a fixed sequence of computations to produce a result, which is ideal for tasks like image classification where you map image features to a class probability in one forward pass. For image classification, this structure is powerful because the network can learn hierarchical representations: early layers detect simple features, later layers combine them into more complex concepts, and the final layer assigns a class. While modern image classifiers often use convolutional structures to handle image data efficiently, those networks remain feedforward in that data flows forward through layers without feedback. The other terms don’t describe this forward-only architecture. The input layer is just the starting point, not the entire forward-pass design. A hidden layer is one component within many networks, not the overall architectural property. Association rules are a data-mining concept unrelated to neural network architectures.