NCA-GENL Trustworthy AI Practice Question
An enterprise deployment of an LLM is exhibiting signs of hallucination where the model generates plausible but factually incorrect technical documentation. Which strategy is most effective for improving factual grounding within the NVIDIA NeMo framework?
⚠ Common exam trap
Test-takers often recommend retraining the model or increasing parameter size to fix hallucinations, overlooking that Retrieval-Augmented Generation (RAG) is the most effective way to ground responses using external data.
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 a RAG pipeline to inject context from a verified vector database.
Retrieval-Augmented Generation (RAG) is the standard architectural approach to ground LLMs by providing external, verified documentation at inference time. By fetching relevant chunks from a trusted vector database, the model reduces reliance on parametric memory, which is prone to hallucinations. This methodology is essential in high-stakes enterprise environments where accuracy is critical for compliance and operational reliability, ensuring the generated output remains strictly bound to the provided source material.
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 temperature parameter to 1.5 to maximize output diversity.
Why it's wrong here
Increasing temperature beyond 1.0 significantly raises the probability of the model selecting low-likelihood tokens. This leads to increased creative variability but exacerbates hallucination risks, as the model prioritizes linguistic coherence over factual accuracy. For technical grounding, low temperature settings are preferred to maintain stability and deterministic behavior.
- ✗
Fine-tune the model exclusively on a large, uncurated corpus of internet text.
Why it's wrong here
Fine-tuning on uncurated data introduces noise and potential biases, often failing to address specific factual gaps. Large-scale internet corpora lack the domain-specific verification needed for enterprise accuracy. Without high-quality, curated datasets, fine-tuning may actually increase the frequency of confident yet incorrect assertions generated by the model.
- ✓
Implement a RAG pipeline to inject context from a verified vector database.
Why this is correct
RAG enables the model to access external, verified knowledge bases before generating a response. By grounding the generation process in specific, trusted documents, the system significantly decreases the likelihood of hallucinations. This allows organizations to maintain factual integrity without needing frequent, resource-intensive retraining of the foundational model.
- ✗
Remove all system prompts to prevent model bias during the inference phase.
Why it's wrong here
System prompts are essential for defining model behavior, tone, and safety boundaries. Removing them makes the model unpredictable and highly susceptible to prompt injection or erratic generation. A well-crafted system prompt is a fundamental tool for enforcing consistent safety and factual constraints within the LLM architecture.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.