AI0-001 AI Implementation and Operations Practice Question
A support team deploys a retrieval-augmented generation assistant that answers questions from internal policy documents. Users report that the assistant confidently invents policy details that do not appear in any document. The team wants to reduce these fabricated answers without retraining the language model. Which change is MOST effective?
⚠ Common exam trap
The trap here is treating hallucination as a creativity or sampling problem and adjusting temperature, when in a retrieval-augmented system the dominant cause is weak grounding and permissive prompting.
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
✓
Improve retrieval relevance and instruct the model to answer only from retrieved passages, returning a fallback when evidence is absent.
Hallucination in a retrieval-augmented assistant is typically a grounding failure. Improving retrieval relevance delivers the correct source passages, and a prompt that restricts answers to retrieved evidence with a defined fallback prevents the model from filling gaps from its own parameters. Sampling temperature, retrieval volume, and prompt length do not address the underlying grounding problem.
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 number of retrieved documents returned to the prompt without changing ranking quality.
Why it's wrong here
Adding more retrieved passages can dilute the context and introduce irrelevant text that the generator may blend into a plausible-sounding but unsupported answer. Quantity without relevance does not improve grounding and can worsen it. Effective grounding depends on retrieval precision and instructing the model to answer only from the supplied evidence.
- ✗
Shorten the system prompt so the model has more room for its own reasoning.
Why it's wrong here
Removing prompt instructions weakens the constraints that keep the model tied to retrieved evidence. The assistant would rely more on parametric memory, which is exactly where fabricated policy details originate. Prompt space is not the limiting factor here; grounding discipline and retrieval quality are, so shortening the prompt makes hallucination more likely.
- ✓
Improve retrieval relevance and instruct the model to answer only from retrieved passages, returning a fallback when evidence is absent.
Why this is correct
Fabrication in retrieval-augmented systems most often stems from weak retrieval or prompts that let the model answer freely. Tightening retrieval relevance ensures the correct passages reach the context, and a grounded instruction with an explicit fallback tells the model to decline when evidence is missing. This directly reduces invented policy details without retraining.
- ✗
Raise the model's temperature setting to make responses more varied.
Why it's wrong here
Temperature controls randomness in token selection. Raising it makes output more diverse and unpredictable, which increases rather than reduces the chance of fabricated content. Hallucination in a retrieval-augmented system is usually driven by weak grounding or an over-permissive prompt, not by low sampling diversity, so this change moves in the wrong direction.
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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.