- A
Adversarial training during model development
Adversarial training incorporates adversarial examples into the training set, making the model more robust to such perturbations at inference time.
- B
Gradient masking to hide model gradients
Why wrong: Gradient masking is a weak defense that can often be bypassed by adaptive attacks; it is not considered a robust defense.
- C
Input sanitization techniques such as JPEG compression or denoising
Input sanitization can remove or reduce the imperceptible perturbations that characterize adversarial examples.
- D
Homomorphic encryption of input images
Why wrong: Homomorphic encryption is for privacy-preserving computation, not for defending against adversarial examples.
- E
Federated learning to train on distributed data
Why wrong: Federated learning is a distributed training approach for privacy, not a defense against adversarial inputs.
AI0-001 AI Security Practice Question
This AI0-001 practice question tests your understanding of ai security. 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.
A company is deploying a pre-trained image classification model for facial recognition in a security system. They are concerned about adversarial examples. Which TWO of the following are effective defenses against adversarial examples?
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
Adversarial training during model development
Adversarial training (including the model with adversarial examples during training) and input sanitization (e.g., JPEG compression, denoising) are proven defenses against adversarial perturbations. Gradient masking is a weak defense. Homomorphic encryption and federated learning are unrelated to adversarial robustness.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Adversarial training during model development
Why this is correct
Adversarial training incorporates adversarial examples into the training set, making the model more robust to such perturbations at inference time.
Related concept
CIDR notation defines the prefix length.
- ✗
Gradient masking to hide model gradients
Why it's wrong here
Gradient masking is a weak defense that can often be bypassed by adaptive attacks; it is not considered a robust defense.
- ✓
Input sanitization techniques such as JPEG compression or denoising
Why this is correct
Input sanitization can remove or reduce the imperceptible perturbations that characterize adversarial examples.
Related concept
CIDR notation defines the prefix length.
- ✗
Homomorphic encryption of input images
Why it's wrong here
Homomorphic encryption is for privacy-preserving computation, not for defending against adversarial examples.
- ✗
Federated learning to train on distributed data
Why it's wrong here
Federated learning is a distributed training approach for privacy, not a defense against adversarial inputs.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI0-001 subnetting questions on CIDR, address ranges, and subnet selection.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security — This question tests AI Security — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Adversarial training during model development — Adversarial training (including the model with adversarial examples during training) and input sanitization (e.g., JPEG compression, denoising) are proven defenses against adversarial perturbations. Gradient masking is a weak defense. Homomorphic encryption and federated learning are unrelated to adversarial robustness.
What should I do if I get this AI0-001 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related AI0-001 subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
About these practice questions
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Last reviewed: Jul 4, 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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