NCA-GENL Trustworthy AI Practice Test 3 — 15 Questions

Question 1

A healthcare AI team is using NVIDIA NeMo to fine-tune a clinical summarization model. They want to ensure that the model does not inadvertently learn to associate certain demographic groups with negative health outcomes present in the training data. Which technique should they apply during fine-tuning to mitigate this bias?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Adversarial debiasing by adding a bias classifier that penalizes demographic predictability. Adversarial debiasing is a targeted method to reduce unwanted correlations between model representations and sensitive attributes. By training an adversary to predict demographics and simultaneously optimizing the main model to fool it, the model learns fairer representations. In clinical summarization, this helps prevent the model from associating certain groups with negative outcomes, directly supporting Trustworthy AI.

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Regularization by adding L2 weight decay to all layers during fine-tuning

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Post-processing calibration by adjusting predicted probabilities per demographic group

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Data augmentation by oversampling examples from underrepresented demographic groups

Question 2

A financial institution is using an LLM to generate investment summaries. To comply with regulations, they must ensure that the model does not produce discriminatory language based on protected attributes. Which Trustworthy AI principle does this requirement primarily address?

Accepted Answer

Fairness. The requirement to avoid discriminatory language based on protected attributes is a core aspect of fairness in Trustworthy AI. Fairness ensures equitable treatment and non-discrimination, making it the correct principle. Other principles like explainability, robustness, and privacy address different aspects of trustworthiness.

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Robustness

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Privacy

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Explainability

Question 3

A retail company is deploying an LLM-based chatbot that answers customer questions about product warranties. The legal team requires that the chatbot never provides legally binding interpretations of warranty terms. Which Trustworthy AI principle is primarily addressed by implementing a content filter that blocks responses containing definitive legal advice?

Accepted Answer

Safety. Safety is the Trustworthy AI principle that ensures AI systems avoid causing harm. By filtering out legally binding warranty interpretations, the chatbot avoids potential legal and financial harm to customers and the company. The other principles address different aspects: accountability is about responsibility, robustness about resilience, and explainability about understanding model decisions.

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Accountability

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Robustness

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Explainability

Question 4

A healthcare analytics team wants to fine-tune an NVIDIA-hosted LLM on patient records. Before training begins, the privacy officer asks what technical measure will prevent the model from memorizing and later reproducing individual patient identifiers. Which measure best addresses this concern?

Accepted Answer

Apply differential privacy during fine-tuning to bound the influence of any single training record.. Memorization of sensitive identifiers is a training-time phenomenon, so the mitigation must operate during fine-tuning. Differential privacy injects noise that mathematically limits how much any individual record can influence the resulting weights, providing a quantifiable privacy guarantee. The other options either worsen memorization or address unrelated concerns such as serving throughput or inference context length.

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Deploy the fine-tuned model behind an API gateway that enforces rate limiting per client.

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Use a larger context window during inference to reduce the need for retrieval of patient data.

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Increase the number of training epochs so the model learns the data distribution more thoroughly.

Question 5

A global e-commerce company uses an NVIDIA-powered LLM to generate product descriptions. They notice that for certain regions, the model occasionally produces content that violates local advertising regulations. To ensure Trustworthy AI, what is the most effective approach to prevent such violations?

Accepted Answer

Implement a region-aware content moderation layer that applies jurisdiction-specific rules before output. A region-aware content moderation layer is the most effective because it dynamically applies local rules to the model's output, ensuring compliance without retraining. It centralizes policy updates and can be integrated with NeMo Guardrails or custom filters. This approach balances scalability, cost, and regulatory adherence, directly supporting Trustworthy AI in a global deployment.

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Fine-tune the model separately for each region using local regulatory data

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Deploy a separate LLM for each region, each trained on local data

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Use prompt engineering to instruct the model to avoid prohibited terms for each region

Question 6

A healthcare company is deploying an LLM to assist with clinical documentation. To ensure Trustworthy AI, they must implement measures to detect and mitigate hallucinations that could lead to incorrect medical records. Which two actions should they take? (Choose two.)

Accepted Answer

Deploy a fact-checking module that cross-references generated statements against a trusted medical database.. To mitigate hallucinations in clinical documentation, grounding responses in verified guidelines via RAG and adding a fact-checking module against a trusted database are effective. These actions ensure outputs are based on authoritative sources and verified before use. Other options like larger models, fine-tuning, or higher temperature do not reliably reduce hallucinations and may introduce new risks.

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Set the model's temperature to a higher value to encourage more diverse outputs.

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Use a larger LLM with more parameters to improve factual accuracy.

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Fine-tune the LLM on the company's historical clinical notes.

Question 7

An AI platform team is preparing an LLM for a public-facing legal information assistant. During evaluation, they observe that the model gives systematically different quality answers depending on the dialect used in the prompt. Which action most directly addresses this Trustworthy AI concern?

Accepted Answer

Curate and augment training and evaluation data to include balanced dialect representation, then re-measure quality per dialect.. Dialect-dependent quality differences are a fairness and bias issue rooted in data representation. The durable fix is to rebalance training and evaluation data across dialects and then verify with per-dialect metrics. Prompt instructions, decoding parameters, and output normalization do not alter the learned statistical disparities, so they cannot reliably eliminate the gap or demonstrate improvement to reviewers.

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Restrict the assistant to answering only in a single standardized dialect.

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Lower the model temperature to make responses more deterministic across all users.

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Add a system prompt instructing the model to treat all users equally.

Question 8

A bank uses an NVIDIA NIM microservice to host an LLM for loan pre-screening. Before go-live, the risk team must confirm that the model's outputs are reproducible and that any change in behavior can be traced to a specific model version. Which deployment practice best satisfies this requirement?

Accepted Answer

Pin the NIM container to an immutable image digest and record the model name, digest, and inference parameters in a model registry entry for each release.. Reproducibility and traceability require freezing the exact serving artifact and recording it. An immutable image digest locks the weights, tokenizer, and runtime, while a registry entry maps each release to that digest and its inference parameters. Logging, parallelism, or higher temperature do not bind an output to a specific model version, so they cannot satisfy an audit that must reconstruct which model produced a decision.

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Run the model on two GPUs in a tensor-parallel configuration so responses are identical across replicas.

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Enable verbose prompt logging on the NIM endpoint and retain the logs for 30 days.

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Increase the model's temperature setting so the risk team can observe a wider range of outputs before approving the release.

Question 9

A software company is using an LLM to generate code snippets for developers. During testing, they discover that the model sometimes produces code with security vulnerabilities, such as SQL injection flaws. Which Trustworthy AI principle is most directly violated by this behavior?

Accepted Answer

Security. The generation of code with SQL injection vulnerabilities directly violates the security principle of Trustworthy AI, which requires that AI systems do not introduce exploitable weaknesses. Other principles like transparency, accountability, and privacy address different aspects and are not the primary violation in this scenario.

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Privacy

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Transparency

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Accountability

Question 10

A financial services company deploys an NVIDIA NIM inference microservice for an LLM that drafts internal investment summaries. The security team wants to ensure that the model does not reveal sensitive account numbers that appear in its training data. Which NVIDIA NeMo Guardrails mechanism should be configured to detect and block such disclosures at runtime?

Accepted Answer

An output rail that applies a custom action to scan the model response for sensitive patterns and block or mask them.. Output rails are the correct guardrail type because they inspect the model's generated response before it is returned to the user. By attaching a custom action that scans for account-number patterns, the system can block or mask the disclosure. Input, dialog, and retrieval rails operate at different stages and cannot reliably prevent sensitive data from appearing in the final output.

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A retrieval rail that filters documents from the vector database before they are passed to the model.

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A dialog rail that uses a canonical form to redirect the conversation when a sensitive pattern is detected.

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An input rail that validates user prompts against a blocklist of forbidden terms.

Question 11

A media company uses an LLM to generate summaries of user-submitted articles. Legal counsel requires that the system detect and refuse requests that attempt to extract verbatim copyrighted passages longer than a defined threshold. Which capability should the team implement?

Accepted Answer

Output filtering that compares generated text against source documents and blocks responses exceeding the verbatim length threshold.. The requirement is a measurable output constraint: no verbatim spans above a defined length. Only output filtering that compares generated text against the source and blocks violations enforces that threshold deterministically. Input keyword blocking, larger context windows, and stylistic fine-tuning may influence behavior but cannot guarantee or verify the specific legal limit, leaving the organization exposed.

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Input filtering that rejects any prompt containing words like summarize or excerpt.

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Fine-tuning the model on public-domain summaries so it learns to paraphrase.

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Increasing the model's context window so it can consider the entire source document when summarizing.

Question 12

A global e-commerce company is deploying an LLM-based chatbot to handle customer inquiries. To ensure Trustworthy AI, they must implement a mechanism that allows users to understand why the chatbot provided a specific response, especially for decisions like refund approvals. Which approach best addresses this requirement?

Accepted Answer

Integrate a feature that provides natural language explanations of the chatbot's reasoning, citing the relevant policy or data used.. To meet the explainability requirement, the chatbot should provide natural language explanations that cite the policies or data behind its decisions. This allows users to understand the rationale, especially for consequential actions like refund approvals. Logging, smaller models, or feedback buttons do not directly deliver understandable explanations for specific responses.

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Use a smaller, more interpretable model instead of a large LLM.

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Log all chatbot interactions and make the logs available to users upon request.

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Implement a feedback button that lets users rate the helpfulness of each response.

Question 13

A hospital's AI governance team is reviewing an LLM that drafts discharge summaries from patient notes. Clinicians report the model occasionally invents medication dosages that were never prescribed. The team wants a mitigation that constrains generated output to an approved formulary before any text reaches the clinician. Which approach best satisfies this requirement while keeping the LLM in place?

Accepted Answer

Configure NVIDIA NeMo Guardrails with a retrieval-augmented output rail that validates every generated dosage against the approved formulary and blocks non-matching entries.. The requirement is a runtime constraint that prevents unsanctioned dosages from appearing in generated text. NeMo Guardrails output rails can inspect model responses and validate them against an authoritative formulary retrieved at generation time, blocking or correcting mismatches before display. This keeps the existing LLM while adding a deterministic verification layer. Training changes and interface warnings do not provide that pre-delivery gate.

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Retrain the base LLM from scratch on the hospital's historical discharge summaries to eliminate the fabrication behavior.

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Add a disclaimer banner to the user interface stating that all generated dosages must be independently verified by the clinician.

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Increase the model's temperature setting so it explores more phrasing variations when drafting dosages.

Question 14

A financial services company is deploying an NVIDIA NIM microservice that answers questions about internal loan policies. Compliance requires that every response be traceable to the exact source paragraph, and that unsupported claims never reach the user. Which approach best enforces this requirement at inference time?

Accepted Answer

Enable retrieval-augmented generation with citations and reject any answer whose claims are not grounded in the retrieved passages.. Traceability to an exact source paragraph and prevention of unsupported claims require grounding each answer in retrieved documents and validating that grounding before the response is released. Retrieval-augmented generation with citations supplies the provenance, while a grounding check enforces it. The other approaches either increase variability, embed knowledge without citations, or only record outputs after the fact, none of which meet both compliance conditions simultaneously.

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Fine-tune the base LLM on the full internal loan policy corpus and rely on the fine-tuned weights to reproduce policy text accurately.

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Deploy the model behind an API gateway that logs every prompt and response, then have compliance staff review the logs weekly.

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Increase the model's temperature so that responses draw on a wider range of internal knowledge, improving coverage of loan policy topics.

Question 15

A healthcare startup is fine-tuning an NVIDIA Llama 2 model on patient records to build a clinical summarization assistant. Before training, the team wants to ensure that individually identifiable information cannot be reconstructed from the model. Which data preparation step best supports this Trustworthy AI goal?

Accepted Answer

Apply differential privacy during fine-tuning by adding calibrated noise to the training process.. Differential privacy is the only option that offers a formal, quantifiable guarantee against re-identification. By injecting calibrated noise during fine-tuning, it limits how much any single patient record can influence the model, making reconstruction attacks provably harder. The other options are either informal heuristics or only remove direct identifiers without addressing model memorization.

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Increase the learning rate to make the model generalize better and forget specific patient details.

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Train the model for fewer epochs to reduce the amount of data it memorizes.

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Use k-anonymity to replace patient names with pseudonyms before fine-tuning.