NCA-GENL Trustworthy AI Practice Question
A financial institution is using an LLM to generate investment summaries. To comply with regulations, they must ensure that the model does not produce discriminatory language based on protected attributes. Which Trustworthy AI principle does this requirement primarily address?
⚠ Common exam trap
The trap here is equating any ethical concern with fairness, when other principles like privacy or robustness might seem related but do not specifically cover non-discrimination.
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
✓
Fairness
The requirement to avoid discriminatory language based on protected attributes is a core aspect of fairness in Trustworthy AI. Fairness ensures equitable treatment and non-discrimination, making it the correct principle. Other principles like explainability, robustness, and privacy address different aspects of trustworthiness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Robustness
Why it's wrong here
Robustness refers to a model's ability to maintain performance under adversarial or unexpected inputs. It does not specifically target discriminatory outputs. The scenario focuses on avoiding bias against protected groups, which is a fairness issue rather than a robustness one.
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Privacy
Why it's wrong here
Privacy involves protecting sensitive personal data and preventing unauthorized disclosure. While related to trust, it does not cover the prohibition of discriminatory language. The requirement here is about equitable treatment, which falls under fairness, not privacy.
- ✓
Fairness
Why this is correct
Fairness in Trustworthy AI ensures that models do not exhibit bias or discriminate against individuals or groups based on protected attributes such as race, gender, or age. The requirement to avoid discriminatory language directly aligns with the fairness principle, which focuses on equitable treatment and non-discrimination in AI outputs.
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Explainability
Why it's wrong here
Explainability concerns the ability to understand and interpret how a model arrives at its decisions. While important, it does not directly address the prohibition of discriminatory language. The scenario emphasizes non-discrimination, which is a fairness concern, not an explainability one.
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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.