NCA-GENL Trustworthy AI Practice Question
Which THREE actions are recommended for establishing a robust 'Human-in-the-Loop' (HITL) system for an AI deployment?
⚠ Common exam trap
Candidates often select passive monitoring options instead of active intervention strategies, such as setting confidence thresholds, creating intuitive review interfaces, and establishing feedback loops.
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
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Setting specific confidence thresholds that trigger human review.
Why this is correct
Confidence thresholds provide a quantitative way to define when an AI is 'unsure.' By routing low-confidence outputs to human experts, organizations can prevent errors from reaching end-users. This mechanism is vital for maintaining high quality and reliability in automated systems, serving as an essential safety gate for deployment.
- ✗
Automating all processes to eliminate the potential for human error.
Why it's wrong here
Eliminating human oversight is often dangerous in complex or high-stakes environments. AI models can exhibit unpredictable behavior in edge cases, and removing humans entirely creates a single point of failure. Human judgment is necessary for handling nuances and unexpected scenarios that the AI has not been trained to manage.
- ✓
Creating intuitive interfaces for humans to edit or approve AI outputs.
Why this is correct
Intervention tools allow humans to efficiently correct or validate AI suggestions. Without intuitive interfaces, the review process becomes a bottleneck, leading to user fatigue or errors. Well-designed UI for HITL processes increases the efficiency of the human expert, making the overall system more responsive and reliable in production.
- ✓
Incorporating expert feedback to refine and improve the model over time.
Why this is correct
The value of HITL extends beyond immediate correction; it provides labeled data that can be used for RLHF or fine-tuning. By feeding human-approved outputs back into the training pipeline, the model continuously learns from expert knowledge, reducing future errors and improving the overall quality and reliability of the AI.
- ✗
Restricting human access to the model's internal weights and architecture.
Why it's wrong here
Human oversight should focus on the output and the reasoning process, not necessarily the raw weights. Restricting access to internal documentation often hinders the ability of human reviewers to understand why a model is failing. Transparency and access to information are key components of effective oversight for Trustworthy AI.
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.