AI0-001 AI Concepts and Techniques Practice Question
A healthcare startup is building a diagnostic support system using a large language model. The system must provide accurate, evidence-based answers and avoid generating harmful or fabricated information. Which THREE techniques should be implemented to achieve this? (Choose 3)
⚠ Common exam trap
AI0-001 often tests whether candidates confuse 'creativity' parameters like temperature with accuracy-enhancing techniques — higher temperature is a distractor that sounds like it improves output but actually worsens factual reliability.
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
✓
Retrieval-Augmented Generation (RAG)
Option A (Retrieval-Augmented Generation, RAG) is correct because grounding the LLM's responses in an external, authoritative medical knowledge base (e.g., PubMed, clinical guidelines) at inference time supplies verifiable evidence and sharply reduces hallucination compared to relying on parametric memory alone. Option C (chain-of-thought prompting) is correct because eliciting intermediate reasoning steps improves the model's accuracy on complex diagnostic questions and makes its conclusions auditable, which supports evidence-based clinical decision support. Option E (fine-tuning on medical textbooks and guidelines) is correct because domain-specific supervised fine-tuning adapts the model's weights to accurate, curated medical content and terminology, raising factual reliability for the healthcare domain. Option B does not belong because disabling output filtering removes safety guardrails and increases the risk of harmful content, the opposite of the stated requirement. Option D does not belong because raising the temperature increases sampling randomness and creativity, which promotes fabricated or inconsistent answers rather than accurate, evidence-based ones.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant medical literature to ground responses.
- ✗
Disabling output filtering to speed up generation
Why it's wrong here
Disabling output filtering removes the safety layer that blocks harmful or fabricated clinical content, directly undermining the accuracy and harm-avoidance requirement. It is tempting because filtering adds latency, and it would be correct only for trusted internal batch workloads where safety review is unnecessary.
- ✓
Using chain-of-thought prompting for reasoning steps
Why this is correct
Chain-of-thought prompting elicits intermediate reasoning steps, improving diagnostic accuracy and making conclusions auditable. It reduces fabricated claims by forcing the model to derive answers transparently, supporting the evidence-based and harm-avoidance requirements of the diagnostic system.
- ✗
Increasing the temperature parameter to encourage creativity
Why it's wrong here
Higher temperature increases sampling randomness, producing varied and fabricated clinical statements, the opposite of evidence-based accuracy. It is tempting because creativity aids brainstorming or patient-education drafting, where varied phrasing is acceptable rather than factual precision.
- ✓
Fine-tuning on medical textbooks and guidelines
Why this is correct
Fine-tuning on medical textbooks and guidelines adapts the model's weights to domain-specific terminology and evidence, reducing fabricated output. It embeds authoritative clinical knowledge directly, satisfying the accuracy and harm-avoidance constraints better than a general-purpose model.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.