NCA-GENL Trustworthy AI Practice Question
Which TWO of the following practices are primary pillars for ensuring AI transparency and explainability in NVIDIA-based LLM deployments?
⚠ Common exam trap
Candidates often select 'model architecture transparency' or 'open-source licensing' as pillars, which are related to accessibility but do not directly ensure explainability or auditability for stakeholders.
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
✓
Publishing detailed model cards documenting data provenance and training limitations.
Transparency and explainability are foundational to building user trust. Providing documented data provenance ensures that stakeholders understand what data influenced the model, while implementing observability tools allows teams to track inputs and outputs for auditing purposes. Together, these practices demystify the 'black box' nature of LLMs, enabling teams to perform root cause analysis on model behaviors and satisfy regulatory requirements regarding algorithmic accountability and fairness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Publishing detailed model cards documenting data provenance and training limitations.
Why this is correct
Model cards provide standardized documentation on the model's intended use, limitations, and the datasets used for training. This transparency is crucial for stakeholders to assess the risks and ethical implications of deploying a specific model, ensuring that the model is applied only within its validated scope.
- ✗
Hard-coding all model responses to ensure they are identical every time.
Why it's wrong here
Hard-coding responses negates the utility of generative AI and fails to address transparency. Explainability is about understanding why a model generates a specific output, not forcing static outputs. True transparency involves analyzing the decision-making process of the model based on input features and context, not removing generation.
- ✓
Maintaining comprehensive logs of prompts and model outputs for auditability.
Why this is correct
Logging is essential for post-hoc analysis and debugging. By maintaining a clear audit trail, organizations can investigate specific user interactions, identify potential biases or hallucinations, and refine the system accordingly. This satisfies the requirement for accountability in systems that impact human decision-making and business outcomes.
- ✗
Using proprietary, undisclosed algorithms to protect intellectual property.
Why it's wrong here
While IP protection is important, complete lack of disclosure inhibits trust and hampers necessary safety audits. Trustworthy AI requires a balance where safety and technical details are transparent to auditors and users, even if the underlying weights or specific training methodology are handled with restricted access.
- ✗
Removing all human-in-the-loop oversight to increase system throughput.
Why it's wrong here
Human-in-the-loop oversight is a critical control mechanism for identifying and mitigating errors in real-time. Removing humans increases the risk of unmonitored model drift or biased outputs. For high-stakes applications, human review is necessary to validate model outputs and ensure alignment with organizational ethical standards.
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.