- A
Spoofing
Why wrong: Spoofing involves impersonation, not data modification.
- B
Tampering
Tampering covers unauthorized changes to data or code, including training data.
- C
Repudiation
Why wrong: Repudiation deals with denying actions, not data integrity.
- D
Information disclosure
Why wrong: Information disclosure involves data exposure, not modification.
Question 197 of 1,000
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.
An AI security team is mapping threats specific to their ML pipeline using the STRIDE framework. Which threat category is primarily addressed by ensuring that training data is not tampered with?
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
✓
Tampering
Ensuring that training data is not tampered with directly addresses the Tampering threat category in the STRIDE framework. Tampering involves the unauthorized modification of data, and in an ML pipeline, corrupted training data can lead to model poisoning, where the model learns incorrect patterns or backdoors. By protecting the integrity of the training dataset, the team mitigates the risk of adversarial manipulation that could degrade model performance or introduce vulnerabilities.
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.
- ✗
Spoofing
Why it's wrong here
Spoofing involves impersonation, not data modification.
- ✓
Tampering
Why this is correct
Tampering covers unauthorized changes to data or code, including training data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Repudiation
Why it's wrong here
Repudiation deals with denying actions, not data integrity.
- ✗
Information disclosure
Why it's wrong here
Information disclosure involves data exposure, not modification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between Tampering (data integrity) and Spoofing (identity deception), so candidates may confuse 'tampering with data' with 'spoofing a data source' and incorrectly choose Spoofing.
Detailed technical explanation
How to think about this question
In ML pipelines, tampering with training data can manifest as data poisoning attacks, where an adversary injects malicious samples to skew model decision boundaries or embed backdoors. This is distinct from other STRIDE threats because it targets the data integrity layer, often requiring cryptographic hashing (e.g., SHA-256) or digital signatures to verify that datasets have not been altered between collection and training. Real-world examples include the 'Tay' chatbot incident, where adversarial inputs corrupted the model's behavior, though that was more inference-time manipulation; training-time tampering is a more insidious vector.
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
Got this wrong? Here's your next step.
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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: Tampering — Ensuring that training data is not tampered with directly addresses the Tampering threat category in the STRIDE framework. Tampering involves the unauthorized modification of data, and in an ML pipeline, corrupted training data can lead to model poisoning, where the model learns incorrect patterns or backdoors. By protecting the integrity of the training dataset, the team mitigates the risk of adversarial manipulation that could degrade model performance or introduce vulnerabilities.
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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