NCA-GENL Trustworthy AI Practice Question
An organization is deploying an LLM for customer support. To ensure Trustworthy AI, which approach best mitigates the risk of model hallucination while maintaining factual grounding?
⚠ Common exam trap
Candidates often select fine-tuning as the primary solution for hallucinations. While fine-tuning improves style, it does not provide the verifiable source grounding that RAG offers for factual accuracy in dynamic domains.
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 (RAG) using a vector database for source verification.
Retrieval-Augmented Generation (RAG) is the industry standard for grounding LLMs. By injecting validated, domain-specific context into the prompt, the model relies on provided documents rather than latent parameters. This architectural choice is critical for Trustworthy AI because it creates a verifiable audit trail, allowing the system to cite sources for its claims, which significantly reduces the probability of generating nonsensical or fabricated responses in customer-facing interactions.
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 model's temperature parameter to maximum to encourage diverse reasoning.
Why it's wrong here
Increasing the temperature enhances randomness and creative generation, which directly contradicts the goal of factual grounding. Higher temperature values lead to greater variance and unpredictability, making the model significantly more prone to hallucinating details that are not supported by the input context or source documentation provided.
- ✗
Apply fine-tuning on the entire customer support history to memorize expected responses.
Why it's wrong here
Fine-tuning on static datasets creates a 'frozen' knowledge base that becomes outdated quickly. It does not provide the model with a mechanism for real-time verification or external referencing. Relying on memorized patterns often exacerbates hallucinations when the model is asked about current information outside the training distribution.
- ✓
Implement Retrieval-Augmented Generation (RAG) using a vector database for source verification.
Why this is correct
RAG architecture provides the model with external, high-quality data at inference time. By retrieving relevant document chunks, the LLM generates answers based on existing, verifiable facts rather than internal weights. This process grounds the response and provides a clear mechanism to link outputs back to specific source material.
- ✗
Restrict the model to only use pre-computed templates for every possible customer query.
Why it's wrong here
While templates offer absolute control over output, they negate the utility of an LLM. This approach fails to address the complexity of natural language understanding required for modern support scenarios. It is not a viable strategy for scaling intelligent conversational systems and lacks the flexibility required for automation.
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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.