NCA-GENL Trustworthy AI Practice Question
When implementing RLHF (Reinforcement Learning from Human Feedback), why is diversity in the human rater pool essential for Trustworthy AI?
⚠ Common exam trap
Test-takers often assume rater diversity is meant solely to increase dataset size or improve technical coding skill, overlooking its core purpose of aligning the model with diverse human values and reducing cultural bias.
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
✓
To ensure the model aligns with a wide range of human values and reduces bias.
Diversity in the rater pool prevents the model from aligning solely with the cultural or subjective preferences of a single demographic. If the raters are not diverse, the model may inadvertently learn biases that alienate specific user groups or reinforce narrow worldviews. Ensuring a broad range of perspectives during the feedback phase is critical for creating an AI that is universally helpful, respectful, and reflective of a global user base.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To increase the total number of data points, thereby lowering training costs.
Why it's wrong here
Diversity is a qualitative requirement, not a quantitative one. Increasing the number of raters without diversity may just reinforce existing biases at a larger scale. The goal of diversity is to improve the quality and fairness of the alignment, not to reduce costs or increase training volume.
- ✓
To ensure the model aligns with a wide range of human values and reduces bias.
Why this is correct
A diverse rater pool helps identify and mitigate cultural or ideological blind spots in the model. By balancing feedback from various backgrounds, the system becomes more equitable and less prone to systemic bias, which is a fundamental requirement for deploying AI in sensitive, global, and multi-cultural environments.
- ✗
To allow the model to learn multiple languages more efficiently during fine-tuning.
Why it's wrong here
Language proficiency is typically handled by the underlying foundation model training and multilingual datasets. While diverse raters may speak different languages, the primary purpose of rater diversity is ethical alignment and bias mitigation, not linguistic performance or the mechanical efficiency of the training process itself.
- ✗
To optimize the GPU memory usage during the reinforcement learning phase.
Why it's wrong here
Rater demographics have absolutely no impact on GPU memory allocation or hardware utilization. These are entirely separate concerns: one deals with ethical social science and human alignment, while the other deals with computer architecture and resource management. Confusing these two areas is a common error in AI engineering.
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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.