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AIF-C01 Practice Question: Using an LLM to generate customer support…
A company is using an LLM to generate customer support responses. They want to reduce hallucinations and improve the accuracy of the responses. Which TWO approaches 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
✓
Apply Bedrock Guardrails with contextual grounding check
RAG grounds the model in retrieved facts, while Bedrock Guardrails with contextual grounding check validates responses against sources. Both are proven techniques to reduce hallucinations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter to 1.0
Why it's wrong here
Raising temperature to 1.0 increases sampling randomness, widening the output distribution and making hallucinated tokens more likely, not less. Temperature tuning is genuinely useful when you want creative, diverse generations for brainstorming or copywriting, where varied phrasing matters more than factual grounding.
- ✗
Use a smaller model to reduce complexity
Why it's wrong here
Parameter count does not govern factual grounding; smaller models typically have weaker recall of niche facts and hallucinate more, not less. Smaller models are the right pick when latency, cost or edge deployment constraints dominate and the task is narrow, such as classification or simple extraction.
- ✗
Remove all system prompts
Why it's wrong here
Removing system prompts strips the grounding instructions that constrain the model's output, so it cannot reduce hallucination. System prompts are precisely where you specify tone, scope and refusal behaviour. This option would only be considered when testing raw base-model capability without any behavioural guardrails.
- ✓
Apply Bedrock Guardrails with contextual grounding check
Why this is correct
Contextual grounding checks compare each generated response against the retrieved source passages, filtering or blocking output that is unsupported by that reference material. This directly targets hallucination by enforcing evidential grounding, satisfying the requirement to improve response accuracy.
- ✓
Use Retrieval-Augmented Generation (RAG) to retrieve relevant documents
Why this is correct
RAG retrieves relevant documents from a knowledge base and injects them into the prompt as grounding context, so the model answers from supplied evidence rather than parametric memory. This directly reduces hallucination and improves factual accuracy for customer support responses.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.