- A
Fine-tuning the model on a dataset of safe responses
Why wrong: Fine-tuning may help but is not as directly effective as input sanitization or robust model training.
- B
Training the model with adversarial examples of prompt injection
Adversarial training teaches the model to resist injection attempts.
- C
Input sanitization to strip special characters and known injection patterns
Sanitization neutralizes common injection vectors before they reach the LLM.
- D
Increasing the model's temperature setting
Why wrong: Temperature affects randomness, not security.
- E
Using a smaller model for faster inference
Why wrong: Model size does not directly address prompt injection.
Question 375 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 engineer is hardening an LLM application against prompt injection. Which TWO controls are most effective? (Select two.)
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
✓
Training the model with adversarial examples of prompt injection
Option B is correct because adversarial training exposes the LLM to crafted prompt injection attacks during fine-tuning, teaching it to recognize and resist malicious inputs. Option C is correct because input sanitization removes or escapes special characters and known injection patterns (e.g., SQL-like meta-characters, escape sequences) before the prompt reaches the model, reducing the attack surface.
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.
- ✗
Fine-tuning the model on a dataset of safe responses
Why it's wrong here
Fine-tuning may help but is not as directly effective as input sanitization or robust model training.
- ✓
Training the model with adversarial examples of prompt injection
Why this is correct
Adversarial training teaches the model to resist injection attempts.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Input sanitization to strip special characters and known injection patterns
Why this is correct
Sanitization neutralizes common injection vectors before they reach the LLM.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing the model's temperature setting
Why it's wrong here
Temperature affects randomness, not security.
- ✗
Using a smaller model for faster inference
Why it's wrong here
Model size does not directly address prompt injection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that fine-tuning on safe responses (Option A) is a security control, when in fact it only improves output safety, not input robustness, and that increasing temperature (Option D) has no security benefit and can degrade reliability.
Detailed technical explanation
How to think about this question
Adversarial training for prompt injection involves generating attack variants (e.g., role-playing, context switching, delimiter manipulation) and including them in the training set with correct refusal labels, forcing the model to learn decision boundaries that separate benign from malicious prompts. Input sanitization at the application layer can use regex or tokenizer-level filters to strip or escape characters like '|', ';', or 'ignore previous instructions' patterns, but must be carefully designed to avoid breaking legitimate inputs. In real-world deployments, a defense-in-depth approach combining both controls is critical because adversarial training alone may not cover all attack vectors, and sanitization alone can be bypassed by encoded or obfuscated injections.
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.
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 — 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: Training the model with adversarial examples of prompt injection — Option B is correct because adversarial training exposes the LLM to crafted prompt injection attacks during fine-tuning, teaching it to recognize and resist malicious inputs. Option C is correct because input sanitization removes or escapes special characters and known injection patterns (e.g., SQL-like meta-characters, escape sequences) before the prompt reaches the model, reducing the attack surface.
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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