NCA-GENL Trustworthy AI Practice Question
A financial services company is deploying an NVIDIA NIM microservice for a customer-facing loan advisory chatbot. The compliance team requires that every response be traceable to a verified source document, and that any response not grounded in those documents be suppressed. Which approach best satisfies this requirement?
⚠ Common exam trap
The trap here is assuming that fine-tuning alone guarantees factual grounding, when in fact only runtime retrieval and guardrails provide per-response traceability and suppression.
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
✓
Implement retrieval-augmented generation with NeMo Guardrails to enforce grounding and block unverified responses.
The requirement is twofold: responses must be traceable to verified documents, and ungrounded responses must be suppressed. Retrieval-augmented generation provides the grounding by injecting verified source content into the prompt, while NeMo Guardrails enforces runtime output rails that can block or rewrite unsupported answers. This combination directly delivers auditable, source-anchored behavior for a regulated advisory use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the base model on the company's historical loan documents and deploy it without additional runtime controls.
Why it's wrong here
Fine-tuning bakes knowledge into weights but does not provide per-response source attribution or a runtime mechanism to suppress ungrounded answers. The model can still hallucinate confidently after fine-tuning. Without retrieval or guardrails, the compliance team would have no way to verify that a given response maps to a specific verified document.
- ✗
Enable NVIDIA TensorRT-LLM quantization to reduce latency so agents can manually review every response.
Why it's wrong here
Quantization improves inference speed and reduces memory footprint but has no bearing on grounding or traceability. Relying on manual review of every response is neither scalable nor a technical control, and it does not prevent ungrounded content from reaching customers in the interim. This option addresses performance, not the compliance requirement.
- ✗
Increase the model temperature to encourage more creative and comprehensive answers.
Why it's wrong here
Raising temperature increases randomness in token sampling, which makes outputs less predictable and more prone to ungrounded generation. It directly undermines the goal of traceable, source-grounded responses. In a regulated loan advisory context, higher variability would make compliance auditing harder, not easier, and would not provide any mechanism to suppress responses lacking a verified source.
- ✓
Implement retrieval-augmented generation with NeMo Guardrails to enforce grounding and block unverified responses.
Why this is correct
Retrieval-augmented generation supplies the model with verified source documents at inference time, and NeMo Guardrails can enforce output rails that block or rewrite responses not supported by retrieved context. Together they deliver the traceability and suppression behavior the compliance team requires, making this the most direct fit for the scenario.
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 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.