→ A basic artificial neural network (ANN) consists of:
An input layer to receive data
At least one hidden layer to process the data
An output layer to produce predictions
These three layers form the minimal architecture required for learning and transformation.
Why the other options are incorrect:
A: Pooling layers are used in CNNs, not core ANN structure.
B: Convolutional layers are specific to CNNs.
D: Dropout is a regularization technique, not a required component.
Official References:
CompTIA DataX (DY0-001) Study Guide – Section 4.3:“ANNs must include an input layer, hidden layer(s), and an output layer to form a complete learning structure.”
Deep Learning Fundamentals, Chapter 3:“At a minimum, a neural network includes input, hidden, and output layers to process and propagate data.”
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