20+ practice questions focused on Trustworthy AI — one of the most tested topics on the NVIDIA Certified Associate: Generative AI LLMs exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Trustworthy AI PracticeWhich TWO of the following are primary pillars of the NIST AI Risk Management Framework, as applied to NVIDIA's approach for Trustworthy AI?
Explanation: The NIST AI Risk Management Framework identifies 'Govern' and 'Map' as foundational pillars for responsible AI development. 'Govern' establishes the culture and processes for accountability, while 'Map' focuses on identifying and assessing the context and risks of the AI system. Mastering these pillars is essential for organizations to move from abstract AI ethics to actionable risk management that ensures AI safety and security throughout the model lifecycle.
Refer to the exhibit. An enterprise developer is implementing an NVIDIA NeMo Guardrails configuration. Which component of this JSON policy most directly addresses the risk of malicious user prompts designed to bypass model constraints?
Explanation: The 'prompt_injection_defense' key is the explicit control mechanism within the configuration designed to detect and block adversarial attempts to manipulate the model's behavior. In the context of Trustworthy AI, prompt injection is a critical threat vector where users attempt to 'jailbreak' the model. By enabling this defense, developers ensure that user inputs are validated against safety policies, preventing unauthorized instructions from overriding the model's core operational guidelines.
A media company is using NVIDIA NeMo Guardrails to secure an LLM that generates article drafts. The security team wants to prevent the model from reproducing copyrighted text from its training data. Which TWO techniques should be integrated to reduce the risk of verbatim regurgitation? (Choose two.)
Explanation: Output filtering with n-gram matching directly detects and blocks verbatim reproduction at inference time, while differential privacy during fine-tuning reduces memorization of training data. Together, they provide a layered defense against copyright regurgitation. The other options are either unreliable (temperature), irrelevant (quantization), or incomplete (data curation alone), so they do not adequately mitigate the risk.
An enterprise is deploying an LLM for legal document review. To ensure Trustworthy AI, they must address potential biases that could lead to unfair treatment of certain parties. Which two techniques are most appropriate for detecting and mitigating bias in this context? (Choose two.)
Explanation: Counterfactual fairness testing directly probes for bias by altering demographic attributes and observing output changes, while SHAP/LIME provide feature-level explanations to uncover biased features. Together, they enable both detection and understanding of bias in legal document review. These techniques are actionable and align with Trustworthy AI principles, unlike privacy-preserving methods that do not target fairness.
A hospital is deploying an LLM assistant that summarizes patient notes and proposes follow-up actions. The compliance team requires that clinicians retain final authority over every clinical decision. Which two measures best enforce this human-oversight requirement? (Choose two.)
Explanation: Enforcing human oversight requires both a structural gate and a content constraint. Requiring explicit clinician confirmation before any order or note is committed ensures no autonomous action, while an output rail that blocks out-of-range dosages prevents unsafe suggestions from ever reaching that gate. Logging, thresholding, and style fine-tuning support quality or accountability but do not themselves place a human in the decision path.
+15 more Trustworthy AI questions available
Practice all Trustworthy AI questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Trustworthy AI. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Trustworthy AI questions on the NCA-GENL frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Trustworthy AI is tested as part of the NVIDIA Certified Associate: Generative AI LLMs blueprint. Practicing with targeted Trustworthy AI questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free NCA-GENL practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Trustworthy AI is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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