The answer is to identify and remove the fake user profiles from the training dataset, then retrain the model. This is correct because data poisoning attack remediation AI requires directly excising the injected malicious samples—in this case, the fraudulent profiles that skewed click-through rates—to restore the integrity of the training distribution before the model can learn properly again. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding that poisoning attacks corrupt the training data itself, unlike evasion attacks which manipulate inputs at inference time; a common trap is confusing adversarial training (which defends against evasion) with the need for data sanitization here. Remember the mnemonic “Poison in, poison out—cut the source, retrain the route” to recall that removing the poisoned data is the first, most direct remediation step.
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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 large e-commerce company uses a recommendation engine trained on millions of user interactions. Recently, the marketing team noticed a sharp increase in click-through rates for a particular product category. Upon investigation, an engineer found that a competitor had injected fake user profiles that consistently clicked on their products, skewing the training data. The company needs to remediate the attack and prevent future occurrences. The team has limited time and budget. Which course of action should the company take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Identify and remove the fake user profiles from the training dataset, then retrain the model
Option A is correct because removing the injected data and retraining directly removes the poison and restores model integrity. Option B is wrong because adversarial training focuses on evasion attacks, not poisoning. Option C is wrong because adding noise does not remove existing poisoned data. Option D is wrong because reducing retraining frequency does not address the attack and could allow more damage.
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.
✓
Identify and remove the fake user profiles from the training dataset, then retrain the model
Why this is correct
This directly eliminates the poisoned data and restores model accuracy.
Clue confirmation
The clue word "first" in the question point toward this answer.
Implement adversarial training to make the model robust to future poisoning attempts
Why it's wrong here
Adversarial training is for evasion, not poisoning; it may not clean existing data.
✗
Decrease the frequency of model retraining to limit exposure to new data
Why it's wrong here
Reducing retraining frequency may allow the attack to persist longer.
✗
Add differential privacy noise to the training data to mask the injected profiles
Why it's wrong here
Differential privacy adds noise but does not remove existing poisoned data.
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.
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Identify and remove the fake user profiles from the training dataset, then retrain the model — Option A is correct because removing the injected data and retraining directly removes the poison and restores model integrity. Option B is wrong because adversarial training focuses on evasion attacks, not poisoning. Option C is wrong because adding noise does not remove existing poisoned data. Option D is wrong because reducing retraining frequency does not address the attack and could allow more damage.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
CIDR notation defines the prefix length.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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. A social media company uses an AI content moderation system to filter hate speech. The system uses a natural language processing model trained on user reports. Recently, the model's false positive rate has increased, blocking legitimate posts. An internal audit reveals that a coordinated group of users has been falsely reporting harmless posts, causing the model to learn incorrect patterns. The company needs to address the attack and restore accuracy. The engineering team can modify the training pipeline. What is the most effective first step?
hard
✓ A.Redesign the training pipeline to incorporate a reputation system for reporting users
B.Increase the weight of non-reported posts to counteract the reported posts' influence
C.Apply adversarial training to make the model robust to crafted inputs
D.Retrain the model on a dataset that excludes all user-reported posts
Why A: Option D is correct because the attack exploits the reporting system; redesigning it to detect coordinated behavior reduces poisoning. Option A is wrong because retraining on non-reported data may not capture all necessary patterns. Option B is wrong because ignoring reported data removes a valuable signal. Option C is wrong because adversarial training is not designed for this type of poisoning.
Last reviewed: Jun 23, 2026
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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.
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