NCA-GENL Trustworthy AI Practice Question
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?
⚠ Common exam trap
Candidates often suggest 'retraining on a fixed schedule,' which is inefficient and ignores the fact that drift can happen unpredictably based on evolving user data or world events.
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
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hard-code all possible responses to prevent the model from learning new patterns.
Why it's wrong here
Hard-coding effectively disables the generative capabilities of the LLM. It prevents the model from 'drifting,' but it also prevents it from functioning as an intelligent assistant. This approach is not a solution to drift but a complete abandonment of the AI's core functionality and utility.
- ✓
Implement continuous evaluation metrics to detect performance degradation.
Why this is correct
Continuous evaluation provides the visibility required to identify when model performance begins to slip. By tracking metrics on representative samples over time, developers can proactively respond to drift. This is the professional standard for maintaining long-term reliability and ensuring the system does not become outdated or inaccurate.
- ✗
Increase the number of parameters in the model to improve its reasoning capacity.
Why it's wrong here
Increasing model size does nothing to mitigate drift; if the underlying data distribution has shifted, a larger model will simply be a larger, more confident, but still incorrect model. Drift is a temporal and distributional issue, not an issue that can be solved by throwing more parameters at it.
- ✗
Disable the model's feedback collection to avoid processing incorrect user data.
Why it's wrong here
Disabling feedback removes the very data needed to identify that drift is occurring. You cannot fix what you cannot measure. Ignoring user data is a reactive strategy that ensures the model will eventually fail, as it will be completely disconnected from the actual needs and language of the users.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.