AI0-001 AI Concepts and Foundations Practice Question
Which TWO of the following are common activation functions used in neural networks? (Choose two.)
⚠ Common exam trap
CompTIA often tests the distinction between activation functions and other neural network components like optimizers (gradient descent), architectures (LSTM), or regularization techniques (dropout), expecting candidates to recognize that only ReLU and Sigmoid directly compute a neuron's output from its input.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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ReLU
ReLU (Rectified Linear Unit) is a widely used activation function that outputs the input directly if it is positive, and zero otherwise, introducing non-linearity while mitigating the vanishing gradient problem. Sigmoid is another common activation function that maps any real-valued input to a value between 0 and 1, making it useful for binary classification output layers. Both are fundamental building blocks in neural network architectures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Gradient descent
Why it's wrong here
Gradient descent is an optimisation algorithm that updates weights by minimising a loss function; it does not transform a neuron's weighted sum into its output. It is the right choice when the question asks how a network learns, not which activation functions it uses.
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LSTM
Why it's wrong here
LSTM is a recurrent network architecture with gated memory cells, not an activation function applied within a neuron. It is correct when the scenario requires modelling long-range sequential dependencies, such as time-series forecasting, rather than choosing a nonlinearity.
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Dropout
Why it's wrong here
Dropout is a regularisation technique that randomly deactivates neurons during training to reduce overfitting; it does not compute a neuron's output. It is the right answer when the question asks how to prevent overfitting, not which activation functions are common.
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ReLU
Why this is correct
ReLU (Rectified Linear Unit) is a standard activation function, outputting the input directly when positive and zero otherwise. Its piecewise-linear form satisfies the question's requirement for common neural network activations, alongside sigmoid and tanh, by enabling efficient gradient propagation during training.
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Sigmoid
Why this is correct
Sigmoid squashes any input into the range zero to one, giving a smooth, differentiable curve. That bounded output suits binary classification and historically trained shallow networks, making it a standard activation function alongside ReLU and tanh.
About these practice questions
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This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.