AI0-001 Machine Learning and Deep Learning Practice Question
Which TWO of the following are common activation functions used in deep neural networks?
⚠ Common exam trap
CompTIA AI often tests the distinction between machine learning algorithms (like Linear Regression, SVM, K-means) and neural network components (like activation functions), so candidates mistakenly select algorithms as activation functions because they recognize them as common ML terms.
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 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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear Regression
Why it's wrong here
Linear regression is a model, not an activation function.
- ✗
Support Vector Machine
Why it's wrong here
SVM is a machine learning algorithm, not an activation function.
- ✗
K-means
Why it's wrong here
K-means is a clustering algorithm.
- ✓
ReLU
Why this is correct
ReLU is the most common activation for hidden layers.
- ✓
Sigmoid
Why this is correct
Sigmoid is a classic activation function for output layers.
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.