NCA-GENL Trustworthy AI Practice Question
A global e-commerce company is deploying an LLM-based chatbot to handle customer inquiries. To ensure Trustworthy AI, they must implement a mechanism that allows users to understand why the chatbot provided a specific response, especially for decisions like refund approvals. Which approach best addresses this requirement?
⚠ Common exam trap
A common mix-up: candidates confuse auditability or user feedback with explainability, assuming that any transparency mechanism satisfies the need for understandable reasoning.
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
✓
Integrate a feature that provides natural language explanations of the chatbot's reasoning, citing the relevant policy or data used.
To meet the explainability requirement, the chatbot should provide natural language explanations that cite the policies or data behind its decisions. This allows users to understand the rationale, especially for consequential actions like refund approvals. Logging, smaller models, or feedback buttons do not directly deliver understandable explanations for specific responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a smaller, more interpretable model instead of a large LLM.
Why it's wrong here
Smaller models may be more interpretable in some cases, but they often lack the capability to handle complex customer inquiries effectively. Moreover, interpretability does not automatically provide explanations for specific decisions. The requirement is for explanations of responses, which this approach does not directly deliver, and it may sacrifice performance.
- ✓
Integrate a feature that provides natural language explanations of the chatbot's reasoning, citing the relevant policy or data used.
Why this is correct
Providing natural language explanations that cite the relevant policy or data directly addresses the need for users to understand why a response was given. This enhances transparency and explainability, key Trustworthy AI principles. It allows users to see the rationale behind decisions like refund approvals, building trust and enabling recourse if needed.
- ✗
Log all chatbot interactions and make the logs available to users upon request.
Why it's wrong here
While logging interactions supports auditability, it does not provide immediate understandable explanations for specific responses. Users would need to parse raw logs, which may not clarify the reasoning. This approach falls short of the requirement for users to understand why a particular decision was made in a user-friendly manner.
- ✗
Implement a feedback button that lets users rate the helpfulness of each response.
Why it's wrong here
A feedback button collects user sentiment but does not explain why a response was given. It addresses user satisfaction, not explainability. The requirement is for users to understand the reasoning behind decisions, which feedback alone cannot provide. Thus, it does not meet the Trustworthy AI explainability need.
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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 NVIDIA exam blueprint
This NCA-GENL practice question is part of Courseiva's free NVIDIA 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 NCA-GENL exam.