Question 270 of 1,000

AI Security, Ethics and Governance →hardMultiple ChoiceObjective-mapped

Data Poisoning Remediation: Removing Poisoned Data

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.

A
Identify and remove the fake user profiles from the training dataset, then retrain the model
This directly eliminates the poisoned data and restores model accuracy.
B
Implement adversarial training to make the model robust to future poisoning attempts
Why wrong: Adversarial training is for evasion, not poisoning; it may not clean existing data.
C
Decrease the frequency of model retraining to limit exposure to new data
Why wrong: Reducing retraining frequency may allow the attack to persist longer.
D
Add differential privacy noise to the training data to mask the injected profiles
Why wrong: Differential privacy adds noise but does not remove existing poisoned data.

Full breakdown with real-world context →

Quick Answer

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.

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

✓

Identify and remove the fake user profiles from the training dataset, then retrain the model

The immediate priority is to remove the poisoned data from the training set and retrain the model, as the fake profiles are actively skewing predictions and causing incorrect click-through rate spikes. This direct remediation addresses the root cause with minimal time and budget, aligning with the team's constraints. Without cleaning the data, any further training or defensive measures would still operate on corrupted inputs.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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.
Related concept
Read the scenario before looking for a memorised 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: answer the scenario, not the keyword

Cisco often tests the principle that immediate incident response (clean and retrain) must precede long-term defenses, tempting candidates to choose sophisticated solutions like adversarial training or differential privacy that are premature without first removing the poisoned data.

Detailed technical explanation

How to think about this question

Data poisoning attacks like this exploit the model's reliance on user interaction signals; removing the injected profiles requires analyzing behavioral patterns (e.g., identical click sequences or anomalous account creation timestamps) to isolate and delete them. Retraining from a clean baseline ensures the model forgets the poisoned associations, which is critical because gradient-based learning can retain subtle biases even after partial data removal. In real-world scenarios, such attacks often target recommendation systems to manipulate rankings, and rapid cleanup is essential to restore trust in metrics.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI0-001 question test?

AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..

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 — The immediate priority is to remove the poisoned data from the training set and retrain the model, as the fake profiles are actively skewing predictions and causing incorrect click-through rate spikes. This direct remediation addresses the root cause with minimal time and budget, aligning with the team's constraints. Without cleaning the data, any further training or defensive measures would still operate on corrupted inputs.

What should I do if I get this AI0-001 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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?

Read the scenario before looking for a memorised answer.

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 →

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Same concept, more angles

1 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. 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 A is correct because the root cause is a coordinated attack where malicious users exploit the reporting mechanism to poison the training data. Incorporating a reputation system into the training pipeline allows the model to weigh or filter user reports based on the trustworthiness of the reporting user, directly mitigating the impact of false reports without discarding legitimate feedback. This addresses the adversarial behavior at the source, restoring accuracy by ensuring the model learns from reliable signals.

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