- A
Apply input sanitization to inference-time queries
Why wrong: Input sanitization at inference time defends against adversarial examples and prompt injection, not against data poisoning which occurs during training.
- B
Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers
Robust aggregation techniques reduce the impact of maliciously inserted outliers on the model's learned parameters.
- C
Validate and clean training data to remove anomalies and outliers
Thorough data validation and cleaning help identify and remove poisoned samples before they influence model training.
- D
Restrict training data sources to trusted, verified providers only
Limiting data sources reduces the attack surface for an adversary to inject poisoned data into the training pipeline.
- E
Implement differential privacy during model training
Why wrong: Differential privacy protects against membership inference, not data poisoning; it does not prevent an attacker from corrupting training data.
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 building an AI-based resume screening tool. They want to ensure the system is secure against data poisoning attacks during the training phase. Which THREE of the following are appropriate defensive measures?
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
Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers
Option B is correct because robust statistical methods like trimmed mean reduce the influence of outlier data points that could be injected by an adversary during training. By discarding extreme values, the model becomes less sensitive to poisoned samples, which is a key defense against data poisoning attacks that aim to corrupt the learned parameters.
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.
- ✗
Apply input sanitization to inference-time queries
Why it's wrong here
Input sanitization at inference time defends against adversarial examples and prompt injection, not against data poisoning which occurs during training.
- ✓
Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers
Why this is correct
Robust aggregation techniques reduce the impact of maliciously inserted outliers on the model's learned parameters.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Validate and clean training data to remove anomalies and outliers
Why this is correct
Thorough data validation and cleaning help identify and remove poisoned samples before they influence model training.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Restrict training data sources to trusted, verified providers only
Why this is correct
Limiting data sources reduces the attack surface for an adversary to inject poisoned data into the training pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Implement differential privacy during model training
Why it's wrong here
Differential privacy protects against membership inference, not data poisoning; it does not prevent an attacker from corrupting training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between training-phase attacks (data poisoning) and inference-phase attacks (evasion), so candidates mistakenly apply inference-time defenses like input sanitization to training security.
Detailed technical explanation
How to think about this question
Data poisoning attacks often target the training dataset by inserting crafted samples that shift decision boundaries, such as in label-flipping or backdoor attacks. Robust statistics like trimmed mean or median-of-means estimators are used in gradient aggregation to mitigate Byzantine failures, which is analogous to defending against poisoning in federated learning or centralized training. In practice, a trimmed mean discards the top and bottom k% of gradient values before averaging, limiting the impact of any single poisoned update.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security — This question tests AI Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers — Option B is correct because robust statistical methods like trimmed mean reduce the influence of outlier data points that could be injected by an adversary during training. By discarding extreme values, the model becomes less sensitive to poisoned samples, which is a key defense against data poisoning attacks that aim to corrupt the learned parameters.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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