NCA-GENL Trustworthy AI Practice Question
Which THREE of the following strategies are recommended by NVIDIA to mitigate data leakage in enterprise-grade LLM applications?
⚠ Common exam trap
Students often pick only one layer of defense, such as just output filtering, forgetting that comprehensive data leakage mitigation requires a defense-in-depth approach across pre-training, inference, and access control.
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
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Applying PII masking techniques on raw data prior to the training phase.
Why this is correct
Masking or redacting PII during the data preparation stage ensures that sensitive information is never ingested by the model. This is the most effective way to prevent the model from learning or memorizing private data, thereby eliminating the risk of it being leaked in future generations.
- ✗
Sharing the entire training dataset publicly to increase model transparency.
Why it's wrong here
Publicly sharing training datasets containing sensitive information is a major security violation. Transparency should be achieved through metadata and documentation rather than exposing raw, sensitive user data. Proper data governance requires keeping training sets secure and protected from unauthorized access at all times.
- ✓
Deploying regex-based output filters to detect and redact sensitive patterns.
Why this is correct
Regex-based filters act as a final safety check for generated content. Even if a model accidentally produces sensitive information, the output guardrail intercepts the message and redacts the sensitive pattern, ensuring that data does not reach the end user in an insecure format.
- ✓
Implementing role-based access control (RBAC) to limit who can query the model.
Why this is correct
RBAC ensures that only authorized users have access to sensitive models or RAG data sources. By restricting access, the organization reduces the attack surface and ensures that sensitive data is only processed for users who have a legitimate business need to interact with that specific information.
- ✗
Increasing the model parameter count to improve internal data encryption.
Why it's wrong here
Increasing parameter count does not provide encryption or security. In fact, larger models may be more prone to memorizing sensitive training data. Security must be managed through external controls, data hygiene, and policy-based guardrails, not through the architectural size or complexity of the underlying LLM itself.
About these practice questions
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JA
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
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.