AI0-001 Machine Learning and Deep Learning Practice Question
Which THREE are common activation functions used in neural networks? (Choose THREE.)
⚠ Common exam trap
CompTIA often tests the distinction between activation functions used in hidden layers versus output layers, so candidates mistakenly select Softmax as a general activation function when it is only appropriate for the final layer in classification tasks.
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 common activation function in neural networks because it introduces non-linearity while being computationally efficient. It outputs the input directly if positive, otherwise zero, which helps mitigate the vanishing gradient problem compared to sigmoid or tanh. This makes it a default choice for hidden layers in many deep learning 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.
- ✓
ReLU
Why this is correct
Rectified Linear Unit is widely used in hidden layers.
- ✗
Softmax
Why it's wrong here
Softmax is used for multi-class output, but the question asks for common activation functions; it's less common in hidden layers.
- ✓
Sigmoid
Why this is correct
Sigmoid is common for binary classification output and hidden layers.
- ✗
Linear
Why it's wrong here
Linear activation is rarely used in hidden layers because it makes the network linear.
- ✓
Tanh
Why this is correct
Hyperbolic tangent is used in hidden layers, often in RNNs.
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Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are common activation functions used in deep neural networks?
easy- A.Linear Regression
- B.Support Vector Machine
- C.K-means
- ✓ D.ReLU
- ✓ E.Sigmoid
Why D: ReLU (Rectified Linear Unit) is a common activation function in deep neural networks because it introduces non-linearity while being computationally efficient, outputting the input directly if positive and zero otherwise. It helps mitigate the vanishing gradient problem, making it a default choice for hidden layers in many architectures.
Variation 2. Which THREE are common activation functions used in neural networks? (Choose three.)
medium- ✓ A.Sigmoid
- B.K-means
- ✓ C.Tanh
- ✓ D.ReLU
- E.Softmax
Why A: Sigmoid is a common activation function in neural networks because it maps any real-valued input to a value between 0 and 1, making it useful for binary classification outputs. It introduces non-linearity and has a smooth gradient, though it suffers from vanishing gradient issues in deep networks.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.