Quick Answer
The correct answer is Sigmoid, and ReLU is the other common activation function. ReLU, or Rectified Linear Unit, outputs the input directly if positive and zero otherwise, introducing non-linearity while mitigating the vanishing gradient problem that plagued earlier networks. Sigmoid maps any real-valued input to a value between 0 and 1, making it ideal for binary classification output layers. On the CompTIA AI+ AI0-001 exam, this question tests your foundational understanding of how neural networks learn; a common trap is confusing activation functions with loss functions or forgetting that ReLU is not a squashing function like Sigmoid. A helpful memory tip: think of ReLU as a “light switch” that turns off negative values, while Sigmoid acts like a “pressure gauge” squeezing everything between 0 and 1.
This AI0-001 practice question tests your understanding of ai concepts and foundations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are common activation functions used in neural networks? (Choose two.)
Gradient descent
Why wrong: Gradient descent is an optimization algorithm.
LSTM
Why wrong: LSTM is a type of recurrent neural network architecture.
Dropout
Why wrong: Dropout is a regularization technique.
ReLU
ReLU is a widely used activation function.
Sigmoid
Sigmoid is a common activation function.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Sigmoid
Why this is correct
Sigmoid is a common activation function.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
ReLU's derivative is 0 for negative inputs and 1 for positive inputs, which avoids saturation and allows gradients to flow effectively in deep networks, but it can cause 'dying ReLU' where neurons become permanently inactive if they always receive negative inputs. Sigmoid suffers from vanishing gradients for extreme input values, as its derivative approaches zero, slowing learning in deep networks; it is often used in the output layer for binary classification because its output can be interpreted as a probability. In practice, ReLU variants like Leaky ReLU or ELU are used to address the dying ReLU problem.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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