NCA-GENL Trustworthy AI Practice Test 2 — 15 Questions

Question 1

Which THREE practices are recommended to minimize 'Data Leakage' in generative AI applications?

Accepted Answer

Automated PII redaction during the data pre-processing phase.. Data leakage occurs when sensitive or private information is inadvertently included in training data or revealed through model outputs. Minimizing this requires PII redaction, strict access control, and output filtering. These measures are essential for Trustworthy AI because protecting user privacy is a foundational responsibility, and failure to control data flow can lead to significant legal, ethical, and reputational damage to an organization.

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Using public, unverified data sources to maximize training diversity.

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Increasing the learning rate to ensure faster convergence and data masking.

Question 2

An organization is concerned about 'Model Drift' affecting the trustworthiness of their customer-facing chatbot. What is the most effective way to monitor and address this issue?

Accepted Answer

Implement continuous evaluation metrics to detect performance degradation.. Model drift occurs when the model's performance degrades over time because the real-world environment changes, making the training data obsolete. Trustworthy AI requires continuous monitoring to detect these performance shifts. By establishing an evaluation pipeline that periodically tests the model against current benchmarks, organizations can identify drift early and trigger retraining, ensuring the system remains accurate, relevant, and reliable in the face of changing user behaviors.

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Hard-code all possible responses to prevent the model from learning new patterns.

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Increase the number of parameters in the model to improve its reasoning capacity.

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Disable the model's feedback collection to avoid processing incorrect user data.

Question 3

Refer to the exhibit. What is the effect of the 'enforcement_mode: strict' configuration on the AI application?

Accepted Answer

The request is rejected if it triggers any items in the block list.. Setting the enforcement mode to 'strict' indicates that the system will block any input that matches the defined block list, with zero tolerance for ambiguity. In the context of Trustworthy AI, this is a high-security posture that prioritizes safety over user experience. It ensures that any attempt to elicit prohibited information results in an immediate and non-negotiable rejection, effectively preventing the model from acting upon harmful or sensitive user requests.

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The model will warn the user about PII but continue processing the request.

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The model will automatically redact the PII and then answer the request.

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The system logs the violation but permits the model to generate a response.

Question 4

Which of the following is a key requirement for achieving 'Transparency' in the context of NVIDIA-certified Generative AI solutions?

Accepted Answer

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.

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Publishing the exact weights of the model to the public internet.

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Eliminating all non-deterministic behaviors to ensure 100% output consistency.

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Ensuring the model always answers with a neutral tone.

Question 5

Which approach is most effective for preventing a model from leaking proprietary information included in its training set?

Accepted Answer

Implementing rigorous data pre-processing and output filtering.. To prevent the leakage of proprietary training data, organizations must employ data-sanitization techniques, such as PII scrubbing and filtering, before training begins. Additionally, during inference, techniques like output-guarding and strict prompt-management help ensure the model does not recover and emit sensitive information. This multi-layered strategy is crucial for Trustworthy AI as it protects the confidentiality of corporate intellectual property while still allowing for the benefits of LLM-based productivity.

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Increasing the model's temperature to make outputs more creative.

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Training the model on larger datasets to dilute the impact of sensitive info.

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Reducing the number of training epochs to stop the model from learning.

Question 6

An enterprise deployment of an LLM is exhibiting signs of hallucination where the model generates plausible but factually incorrect technical documentation. Which strategy is most effective for improving factual grounding within the NVIDIA NeMo framework?

Accepted Answer

Implement a RAG pipeline to inject context from a verified vector database.. Retrieval-Augmented Generation (RAG) is the standard architectural approach to ground LLMs by providing external, verified documentation at inference time. By fetching relevant chunks from a trusted vector database, the model reduces reliance on parametric memory, which is prone to hallucinations. This methodology is essential in high-stakes enterprise environments where accuracy is critical for compliance and operational reliability, ensuring the generated output remains strictly bound to the provided source material.

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Increase the temperature parameter to 1.5 to maximize output diversity.

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Fine-tune the model exclusively on a large, uncurated corpus of internet text.

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Remove all system prompts to prevent model bias during the inference phase.

Question 7

Refer to the exhibit. A developer implements this NVIDIA NeMo Guardrails configuration. A user submits a query about financial advice. What is the expected behavior of the LLM?

Accepted Answer

The guardrail blocks the request because finance is not in the whitelist.. The configured guardrail includes topic whitelisting, which restricts the model to only discussing 'tech' and 'science'. When the user submits a financial query, the system identifies the topic mismatch. Since the policy does not explicitly allow financial topics, the guardrail intercepts the input, preventing the model from generating a response. This proactive filtering is a key component of trustworthy AI, ensuring models operate within predefined domain boundaries.

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The model generates a generic response about finance using its internal knowledge.

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The system processes the request because the toxicity score is below 0.85.

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The model prompts the user to clarify if the finance query is related to technology.

Question 8

Which TWO of the following practices are primary pillars for ensuring AI transparency and explainability in NVIDIA-based LLM deployments?

Accepted Answer

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.

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Hard-coding all model responses to ensure they are identical every time.

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Using proprietary, undisclosed algorithms to protect intellectual property.

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Removing all human-in-the-loop oversight to increase system throughput.

Question 9

When evaluating LLMs for bias, what is the primary purpose of conducting a 'red teaming' exercise?

Accepted Answer

To identify vulnerabilities and edge cases that could lead to biased or harmful output.. Red teaming is a deliberate effort to stress-test the model by attempting to force it to output harmful, biased, or restricted content. By simulating adversarial attacks, developers can uncover latent vulnerabilities and systemic biases that standard testing might miss. This proactive evaluation is essential for building trustworthy systems, as it allows developers to implement necessary guardrails and safety filters before the model is deployed to production, thereby minimizing real-world harm.

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To increase the speed of model inference in production environments.

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To automate the generation of training data for fine-tuning the model.

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To reduce the number of tokens required for long-form generation tasks.

Question 10

Which THREE of the following strategies are recommended by NVIDIA to mitigate data leakage in enterprise-grade LLM applications?

Accepted Answer

Applying PII masking techniques on raw data prior to the training phase.. Data leakage occurs when sensitive information is unintentionally included in the model's training set or emitted in its response. Mitigating this requires a defense-in-depth approach: sanitizing training data to remove PII, implementing strict access controls for users, and utilizing output guardrails to intercept sensitive patterns (like credit card numbers) before they reach the user. These measures collectively protect user privacy and corporate integrity, ensuring that sensitive data remains secure throughout the model lifecycle.

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Sharing the entire training dataset publicly to increase model transparency.

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Increasing the model parameter count to improve internal data encryption.

Question 11

A financial services company is deploying an NVIDIA NIM microservice for an internal LLM assistant that summarizes earnings call transcripts. The security team wants to ensure that the model cannot be coerced via prompt injection into revealing confidential merger discussions embedded in prior context. Which NVIDIA-developed safety mechanism should be integrated directly into the inference pipeline to evaluate prompts and responses against a defined policy at runtime?

Accepted Answer

NVIDIA NeMo Guardrails. NeMo Guardrails is purpose-built to enforce safety policies at runtime by intercepting LLM inputs and outputs. It can detect and block prompt injection attempts that try to extract confidential context, which is exactly the risk described. The other options are performance or preprocessing tools that do not evaluate content against a safety policy, so they cannot prevent the leakage scenario.

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NVIDIA Triton Inference Server model ensembles

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NVIDIA TensorRT-LLM quantization

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NVIDIA DALI preprocessing pipelines

Question 12

A financial services company has deployed an NVIDIA NIM microservice hosting a Llama 3 70B model for internal document summarization. The security team wants to ensure that the model's outputs cannot be used to exfiltrate sensitive customer data that may have been memorized during pretraining. Which NVIDIA AI Enterprise feature should be implemented to detect and filter such memorized content in real time?

Accepted Answer

NVIDIA NeMo Guardrails with a custom output rail using a sensitivity classifier. NeMo Guardrails is purpose-built for adding programmable guardrails to LLM applications, including output filtering. A custom output rail can run a sensitivity classifier to detect and block memorized sensitive data before it is returned to the user. This provides a real-time, configurable safeguard that directly addresses the risk of data exfiltration from a deployed NIM microservice.

Answer

NVIDIA Triton Inference Server with dynamic batching and model ensemble

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NVIDIA Morpheus with a pre-trained sensitive information detection model

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NVIDIA TensorRT-LLM with INT8 quantization and kernel fusion

Question 13

An AI team is deploying a Llama 3 70B model for internal knowledge retrieval. They want to ensure that the model's responses are grounded in the company's approved document corpus and that any attempt to elicit unapproved content is blocked. Which NVIDIA NeMo Guardrails component should they configure to define these behavioral constraints?

Accepted Answer

Colang flows that specify dialogue patterns and guardrail actions.. NeMo Guardrails uses Colang to define dialogue flows and guardrail actions that constrain LLM behavior. By writing Colang flows, the team can enforce grounding to approved documents and block attempts to elicit unapproved content. Other components like embedding models or inference servers do not provide this policy enforcement capability.

Answer

A custom embedding model fine-tuned on the document corpus.

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TensorRT-LLM optimization profiles for inference acceleration.

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The NVIDIA Triton Inference Server model ensemble configuration.

Question 14

A healthcare AI team is using NVIDIA NeMo to fine-tune an LLM for clinical note summarization. During evaluation, they notice the model generates different summaries for the same patient note when the note includes demographic descriptors, even though clinical content is identical. The team wants to quantify this behavior systematically before deployment. Which approach should they use to measure the model's sensitivity to demographic attributes?

Accepted Answer

Run a counterfactual fairness test by swapping demographic terms and measuring output divergence.. Counterfactual fairness testing is the established method to detect whether a model's output changes when only a protected attribute is altered. It provides a quantitative measure of demographic sensitivity, which is essential before deploying a clinical summarization model. The other options either change model behavior without measuring bias or fail to isolate the demographic variable, so they do not meet the evaluation requirement.

Answer

Fine-tune the model again on a larger dataset without demographic terms.

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Increase the temperature setting to produce more diverse summaries.

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Apply TensorRT-LLM INT8 quantization to reduce model variance.

Question 15

A financial services company is deploying an NVIDIA NIM microservice for a customer-facing loan advisory chatbot. The compliance team requires that every response be traceable to a verified source document, and that any response not grounded in those documents be suppressed. Which approach best satisfies this requirement?

Accepted Answer

Implement retrieval-augmented generation with NeMo Guardrails to enforce grounding and block unverified responses.. The requirement is twofold: responses must be traceable to verified documents, and ungrounded responses must be suppressed. Retrieval-augmented generation provides the grounding by injecting verified source content into the prompt, while NeMo Guardrails enforces runtime output rails that can block or rewrite unsupported answers. This combination directly delivers auditable, source-anchored behavior for a regulated advisory use case.

Answer

Fine-tune the base model on the company's historical loan documents and deploy it without additional runtime controls.

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Enable NVIDIA TensorRT-LLM quantization to reduce latency so agents can manually review every response.

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Increase the model temperature to encourage more creative and comprehensive answers.