NCA-GENL Trustworthy AI Practice Question
Which of the following is a key requirement for achieving 'Transparency' in the context of NVIDIA-certified Generative AI solutions?
⚠ Common exam trap
Candidates often confuse transparency with model explainability (XAI) or interpretability. While related, transparency specifically refers to the documentation of model constraints and data origins for responsible usage.
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
✓
Documenting model limitations, intended use cases, and data characteristics.
Transparency requires providing clear documentation about model limitations, training data sources, and intended use cases. This is critical for Trustworthy AI because it allows developers and stakeholders to make informed decisions about whether a model is appropriate for a specific task. By being open about what the model can and cannot do, organizations reduce the risk of misuse and build legitimate, evidence-based trust with their users and stakeholders.
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 the exact weights of the model to the public internet.
Why it's wrong here
Transparency does not require open-sourcing the weights, which can present significant security and IP risks. Transparency is about the documentation of capabilities, constraints, and data provenance, not the disclosure of sensitive intellectual property or proprietary model weights that could be used for malicious purposes or competitive exploitation.
- ✓
Documenting model limitations, intended use cases, and data characteristics.
Why this is correct
Clear documentation, often captured in 'Model Cards,' is the standard for transparency. By explicitly stating what a model is designed for, what its known weaknesses are, and the nature of the data it was trained on, developers provide the necessary context for safe and appropriate application deployment.
- ✗
Eliminating all non-deterministic behaviors to ensure 100% output consistency.
Why it's wrong here
Generative models are inherently probabilistic; forcing 100% consistency removes the core value of an LLM. Transparency means clearly communicating that the model is probabilistic, not suppressing the nature of the model itself. Consistency is a performance goal, not a transparency requirement for Trustworthy AI in generative systems.
- ✗
Ensuring the model always answers with a neutral tone.
Why it's wrong here
Tone is a style choice, not a measure of transparency. A model can be transparent and opinionated, or opaque and neutral. Transparency relates to the availability of information about the system's construction and performance metrics, not the stylistic or emotional quality of the text generated by the model.
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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
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