AI0-001 AI Implementation and Operations Practice Question
A media company uses a generative AI assistant to draft customer responses. After an update to the underlying foundation model, agents report that responses sometimes include fabricated policy details. The operations team must detect this regression quickly and prevent fabricated content from reaching customers. Which combination of controls is most appropriate?
⚠ Common exam trap
The trap here is relying on prompt instructions or lagging satisfaction metrics as if they were reliable hallucination detection and prevention controls.
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
✓
Add automated groundedness and hallucination checks against the approved policy knowledge base, and require agent approval before any response is sent.
Detecting fabricated policy details requires verifying generated text against the authoritative source, which groundedness or hallucination checks provide. Preventing delivery requires a gate such as agent approval before sending. Rollback, prompt tweaks, and retrospective sampling either do not detect unsupported claims or act too late to protect customers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Roll back to the previous foundation model version and monitor customer satisfaction scores for improvement.
Why it's wrong here
Rolling back may restore prior behavior, but it abandons the update and relies on satisfaction scores, which are lagging and indirect indicators of hallucination. It does not build detection for fabricated content or a barrier before responses are sent, so the underlying operational gap remains if the model is updated again.
- ✗
Enable request logging and review a sample of conversations weekly for quality issues.
Why it's wrong here
Sampled weekly review is too slow and too sparse to catch a regression quickly, and it occurs after responses have already been sent. Logging supports investigation but provides no automated detection or pre-send prevention, so fabricated policy details could still reach many customers before anyone notices.
- ✗
Lower the model's maximum output token limit and add a system prompt instructing it to be accurate.
Why it's wrong here
Shorter outputs and prompt instructions can reduce some fabrication but do not verify claims against the policy knowledge base. Prompt-level guidance is not a reliable control because the model can still generate plausible unsupported details, and nothing blocks such content before it reaches a customer.
- ✓
Add automated groundedness and hallucination checks against the approved policy knowledge base, and require agent approval before any response is sent.
Why this is correct
Groundedness checks compare generated content against the authoritative knowledge base to flag unsupported claims, and human approval prevents flagged content from reaching customers. Together they provide both automated detection and a safety barrier, which matches the need to catch the regression quickly and block fabricated policy details.
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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.