- A
Ground the model using Retrieval-Augmented Generation (RAG) with curated medical databases
RAG reduces hallucinations by providing the model with relevant, authoritative information at inference time, making it less likely to generate unsupported advice.
- B
Monitor for anomalous inputs to detect data poisoning attempts
Why wrong: Anomaly monitoring detects malicious inputs but does not prevent the model from hallucinating dangerous advice.
- C
Employ federated learning to train on decentralized patient data
Why wrong: Federated learning is a privacy-preserving training method, not a defense against hallucinations.
- D
Implement output filtering and content moderation to block harmful or unverified medical advice
Output filtering and moderation act as a safety layer to catch and prevent dangerous content generated by the model from reaching the user.
- E
Use robust training techniques like adversarial training
Why wrong: Adversarial training improves robustness to adversarial inputs but does not directly prevent hallucinations in standard use.
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 startup is building a medical diagnosis support system using a large language model. To prevent the model from generating harmful advice due to hallucinations, which TWO measures should they implement as part of their AI security strategy?
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
Ground the model using Retrieval-Augmented Generation (RAG) with curated medical databases
Option A is correct because Retrieval-Augmented Generation (RAG) grounds the LLM's outputs in verified, curated medical databases (e.g., PubMed, clinical guidelines). By retrieving relevant, factual information before generating a response, RAG significantly reduces the risk of hallucinations that could lead to harmful medical advice. This is a direct security measure to ensure the model's outputs are factually accurate and safe.
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.
- ✓
Ground the model using Retrieval-Augmented Generation (RAG) with curated medical databases
Why this is correct
RAG reduces hallucinations by providing the model with relevant, authoritative information at inference time, making it less likely to generate unsupported advice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Monitor for anomalous inputs to detect data poisoning attempts
Why it's wrong here
Anomaly monitoring detects malicious inputs but does not prevent the model from hallucinating dangerous advice.
- ✗
Employ federated learning to train on decentralized patient data
Why it's wrong here
Federated learning is a privacy-preserving training method, not a defense against hallucinations.
- ✓
Implement output filtering and content moderation to block harmful or unverified medical advice
Why this is correct
Output filtering and moderation act as a safety layer to catch and prevent dangerous content generated by the model from reaching the user.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use robust training techniques like adversarial training
Why it's wrong here
Adversarial training improves robustness to adversarial inputs but does not directly prevent hallucinations in standard use.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between inference-time security controls (like RAG and output filtering) versus training-time or data-protection measures (like federated learning, adversarial training, or anomaly detection), leading candidates to select options that are valid security techniques but do not directly address the specific threat of hallucinated harmful advice.
Detailed technical explanation
How to think about this question
RAG works by embedding a user query, retrieving the top-k relevant documents from a vector database (e.g., using cosine similarity on embeddings from a model like Sentence-BERT), and then prepending those documents as context to the LLM's prompt. This forces the LLM to condition its generation on retrieved facts, effectively acting as a dynamic knowledge base. In a medical context, this is critical because even a single hallucinated symptom or drug interaction could lead to a misdiagnosis or adverse event, and RAG provides a verifiable chain of evidence for each output.
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: Ground the model using Retrieval-Augmented Generation (RAG) with curated medical databases — Option A is correct because Retrieval-Augmented Generation (RAG) grounds the LLM's outputs in verified, curated medical databases (e.g., PubMed, clinical guidelines). By retrieving relevant, factual information before generating a response, RAG significantly reduces the risk of hallucinations that could lead to harmful medical advice. This is a direct security measure to ensure the model's outputs are factually accurate and safe.
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
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 →
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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