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
✓
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.
- ✗
Gradient descent
Why it's wrong here
Gradient descent is an optimization algorithm.
- ✗
LSTM
Why it's wrong here
LSTM is a type of recurrent neural network architecture.
- ✗
Dropout
Why it's wrong here
Dropout is a regularization technique.
- ✓
ReLU
Why this is correct
ReLU is a widely used activation function.
- ✓
Sigmoid
Why this is correct
Sigmoid is a common activation function.
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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.