NCA-GENL Trustworthy AI Practice Test 1 — 15 Questions

Question 1

Which TWO of the following are primary pillars of the NIST AI Risk Management Framework, as applied to NVIDIA's approach for Trustworthy AI?

Accepted Answer

Govern: Establishing policies and accountability to manage AI-related risks.. 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.

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Monetize: Quantifying the financial impact of AI deployments to stakeholders.

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Optimize: Ensuring maximum hardware utilization through kernel fusion.

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Publicize: Ensuring all training data is released for public scrutiny.

Question 2

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?

Accepted Answer

prompt_injection_defense. 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.

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max_token_length

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safety_filter

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data_sanitization

Question 3

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.)

Accepted Answer

Implement output filtering that detects and blocks long n-gram matches against known copyrighted sources.. 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.

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Fine-tune the model on a dataset that excludes all copyrighted works.

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Apply TensorRT-LLM quantization to reduce model size.

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Increase the model's temperature to encourage more creative outputs.

Question 4

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.)

Accepted Answer

Conduct counterfactual fairness testing by swapping demographic attributes in prompts and comparing outputs. 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.

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Implement k-anonymity on the training dataset to prevent re-identification

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Apply differential privacy during fine-tuning to add noise to the training data

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Perform data augmentation by generating synthetic legal documents with balanced demographic representations

Question 5

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.)

Accepted Answer

Deploy NVIDIA NeMo Guardrails with an output rail that blocks suggestions containing medication dosages outside a reference range.. 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.

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Raise the model's confidence threshold so low-certainty suggestions are suppressed before reaching the clinician.

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Fine-tune the base model on the hospital's historical notes so its summaries match clinician writing style.

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Log every model suggestion and clinician decision to an immutable audit trail with timestamps and user identity.

Question 6

An AI governance team is auditing an LLM deployed for loan approval recommendations. They need to provide regulators with a clear rationale for each decision the model makes, including which input features most influenced the outcome. Which NVIDIA tool or framework should they use to generate feature attribution explanations for the model's predictions?

Accepted Answer

NVIDIA Triton Inference Server with SHAP integration. Triton Inference Server with SHAP integration enables feature attribution explanations for each prediction, which is essential for regulatory audits of loan approvals. SHAP values show how much each input feature contributed to the outcome. The other options are either safety, preprocessing, or optimization tools that do not provide interpretability, so they cannot fulfill the explainability requirement.

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NVIDIA DALI for data preprocessing

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NVIDIA NeMo Guardrails

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NVIDIA TensorRT for model optimization

Question 7

A financial services company is deploying an LLM-powered advisory assistant built on NVIDIA NeMo. The compliance team requires that the model refuse to answer questions about specific competitor products, even if the user rephrases the request multiple ways. Which NeMo Guardrails capability should the developer configure to enforce this restriction most reliably?

Accepted Answer

A canonical form set combined with dialog rails that map matched intents to a predefined refusal response.. NeMo Guardrails uses canonical forms to capture the many ways a user might express the same restricted intent, and dialog rails to deterministically route matched intents to a refusal. Together they enforce a hard compliance rule that must survive paraphrasing, which is exactly the requirement for blocking competitor product questions in a regulated advisory assistant.

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A retrieval-augmented generation (RAG) pipeline that indexes only approved documents.

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A dialog rail that defines canonical forms and uses them to match user intents and trigger a refusal flow.

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An input rail that uses an embedding model and similarity threshold to detect and block restricted topics.

Question 8

An organization is deploying an LLM for customer support. To ensure Trustworthy AI, which approach best mitigates the risk of model hallucination while maintaining factual grounding?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) using a vector database for source verification.. Retrieval-Augmented Generation (RAG) is the industry standard for grounding LLMs. By injecting validated, domain-specific context into the prompt, the model relies on provided documents rather than latent parameters. This architectural choice is critical for Trustworthy AI because it creates a verifiable audit trail, allowing the system to cite sources for its claims, which significantly reduces the probability of generating nonsensical or fabricated responses in customer-facing interactions.

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Increase the model's temperature parameter to maximum to encourage diverse reasoning.

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Apply fine-tuning on the entire customer support history to memorize expected responses.

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Restrict the model to only use pre-computed templates for every possible customer query.

Question 9

Which technique should an organization prioritize to identify and reduce systematic bias in a generative model's training dataset?

Accepted Answer

Conduct a comprehensive audit of the training corpus to identify demographic imbalances.. Bias mitigation must start at the data layer. Analyzing the dataset for representational imbalances, stereotype associations, and lack of diversity is the most effective way to address bias before model training begins. This process is essential for Trustworthy AI because it ensures that the foundational intelligence of the model is not built upon skewed or exclusionary data, which prevents the amplification of societal prejudices in downstream AI applications.

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Apply post-processing filters to censor all sensitive keywords in model output.

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Increase the model size to allow for better internal alignment with human values.

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Use an adversarial model to guess the sensitive attributes of the primary model's output.

Question 10

Which of the following best describes the principle of 'Interpretability' in the context of Trustworthy AI?

Accepted Answer

The capability to explain the internal decision-making process in human-understandable terms.. Interpretability refers to the degree to which a human can understand the cause of a decision or prediction made by an AI model. In high-stakes domains like healthcare or finance, knowing 'why' a model arrived at a conclusion is as important as the conclusion itself. This transparency is crucial for building trust, debugging errors, and ensuring that the model complies with regulatory requirements regarding fairness and decision-making accountability.

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The ability of the model to perform multiple tasks simultaneously without loss of accuracy.

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The speed at which a model can process training data during the fine-tuning phase.

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The process of removing all personal identifiers from the training dataset.

Question 11

Which THREE actions are recommended for establishing a robust 'Human-in-the-Loop' (HITL) system for an AI deployment?

Accepted Answer

Setting specific confidence thresholds that trigger human review.. An effective Human-in-the-Loop system balances AI speed with human judgment. The three critical steps are defining clear escalation triggers, providing tools for human intervention, and maintaining a feedback loop where human corrections improve the model. These actions ensure that humans remain the ultimate authority in high-stakes scenarios, directly supporting the Trustworthy AI goals of oversight, accountability, and continuous improvement through expert guidance.

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Automating all processes to eliminate the potential for human error.

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Restricting human access to the model's internal weights and architecture.

Question 12

Refer to the exhibit. Which concept of Trustworthy AI is primarily demonstrated by the actions shown in the CLI output?

Accepted Answer

Safety and Robustness.. The logs demonstrate 'Safety and Robustness' through active content moderation. By identifying a toxicity score above the acceptable threshold and programmatically blocking the response, the system prevents the dissemination of harmful content. This is a core requirement of Trustworthy AI, ensuring that models operate within defined safety guardrails and actively mitigate the risk of harmful output generation, even when triggered by user input.

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Model Explainability.

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Data Privacy.

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Algorithmic Efficiency.

Question 13

Why is 'Data Provenance' considered a crucial component in maintaining Trustworthy AI?

Accepted Answer

It provides a clear audit trail for data lineage, ethics, and legal compliance.. Data provenance involves tracking the origin, history, and licensing status of the training data. For Trustworthy AI, it ensures legal compliance, intellectual property rights, and the ability to audit the training set for bias. Without a clear chain of custody for the data, organizations cannot guarantee that their models are trained on ethical, high-quality, and legally obtained information, leading to significant reputation and legal risks.

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It ensures that the model can be compressed into a smaller size for edge deployment.

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It speeds up the GPU training process by indexing the data in a vector database.

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It automatically corrects grammatical errors in the training corpus.

Question 14

Which of the following scenarios best represents an 'Adversarial Attack' against an LLM?

Accepted Answer

A user inputting a specifically engineered prompt to bypass content filters.. An adversarial attack occurs when a user intentionally crafts inputs designed to deceive the model into ignoring its safety guidelines or producing unintended behavior. This is a primary concern for Trustworthy AI because it highlights the fragility of models when faced with malicious inputs. Recognizing these patterns is essential for developers to implement robust defensive guardrails that maintain the system's integrity under hostile conditions.

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A user accidentally providing a very long, complex question that causes a memory error.

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The model failing to correctly answer a question because the information is not in its training data.

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A developer updating the model to use a new, more efficient activation function.

Question 15

When implementing RLHF (Reinforcement Learning from Human Feedback), why is diversity in the human rater pool essential for Trustworthy AI?

Accepted Answer

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.

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To increase the total number of data points, thereby lowering training costs.

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To allow the model to learn multiple languages more efficiently during fine-tuning.

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To optimize the GPU memory usage during the reinforcement learning phase.