NCA-GENL Trustworthy AI Practice Question
A healthcare analytics team uses an LLM to summarize patient notes for clinician review. The team observes that summaries for patients from one demographic group systematically omit certain chronic conditions that appear in the source notes. Which action most directly addresses this Trustworthy AI failure?
⚠ Common exam trap
The trap here is treating a systematic, subgroup-specific omission pattern as a general accuracy problem that a bigger model or longer output will solve.
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
✓
Measure summarization completeness per demographic subgroup and retrain or adjust the pipeline until omission rates are comparable across groups.
A subgroup-specific pattern of omitted chronic conditions is a fairness defect that must be quantified before it can be fixed. Measuring completeness per demographic group establishes whether the disparity is real and whether interventions work. Disclaimers, longer summaries, and larger models are generic changes that do not target the measured gap and cannot demonstrate that equitable performance has been achieved in this clinical setting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a larger foundation model with a longer context window so that the entire patient record fits in a single prompt.
Why it's wrong here
A larger model with a longer context window may improve overall summarization quality, but it does not guarantee equitable treatment across demographic groups. Bias can persist or shift in larger models, and without subgroup measurement the team cannot confirm improvement. This is a capability upgrade, not a fairness remediation, and it leaves the observed disparity unverified and potentially intact.
- ✗
Add a disclaimer to every generated summary stating that the output may be incomplete and must be verified by a clinician.
Why it's wrong here
A blanket disclaimer shifts responsibility to the clinician but leaves the systematic omission of chronic conditions in one demographic group unaddressed. Clinicians already verify summaries; the problem is that the model hides information unevenly. Disclaimers do not change model behavior, do not measure the disparity, and would apply equally to all groups, so they cannot close a subgroup-specific completeness gap.
- ✓
Measure summarization completeness per demographic subgroup and retrain or adjust the pipeline until omission rates are comparable across groups.
Why this is correct
The failure is a measurable disparity in information retention across subgroups, so the correct response is to quantify that disparity with subgroup-level completeness metrics and then remediate until the gap closes. Without per-group measurement, the team cannot know whether changes help. This is the direct, evidence-based path to correcting a fairness defect in a clinical summarization pipeline where omissions can affect care.
- ✗
Increase the maximum summary length so that the model has more room to include every condition mentioned in the source note.
Why it's wrong here
Length is not the demonstrated cause; the omissions are concentrated in one demographic group, which points to a data or modeling disparity rather than truncation. If length were the issue, omissions would appear across groups. Raising the limit may add verbosity without fixing the disparity and could even dilute clinically important content, so it does not target the actual failure mechanism.
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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.