NCA-GENL Trustworthy AI Practice Question
A healthcare AI team is using NVIDIA NeMo to fine-tune an LLM for clinical note summarization. During evaluation, they notice the model generates different summaries for the same patient note when the note includes demographic descriptors, even though clinical content is identical. The team wants to quantify this behavior systematically before deployment. Which approach should they use to measure the model's sensitivity to demographic attributes?
⚠ Common exam trap
It's easy for candidates to confuse output diversity with bias measurement; increasing randomness does not quantify sensitivity to protected attributes.
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
✓
Run a counterfactual fairness test by swapping demographic terms and measuring output divergence.
Counterfactual fairness testing is the established method to detect whether a model's output changes when only a protected attribute is altered. It provides a quantitative measure of demographic sensitivity, which is essential before deploying a clinical summarization model. The other options either change model behavior without measuring bias or fail to isolate the demographic variable, so they do not meet the evaluation requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model again on a larger dataset without demographic terms.
Why it's wrong here
Removing demographic terms from training data does not guarantee the model will ignore them at inference, and it does not provide a measurement of current sensitivity. The team first needs to quantify the existing behavior. Retraining without measurement is premature and could mask the problem rather than systematically evaluate it.
- ✓
Run a counterfactual fairness test by swapping demographic terms and measuring output divergence.
Why this is correct
Counterfactual fairness testing directly measures whether changing only a protected attribute alters the model's output. By swapping demographic descriptors while keeping clinical content constant, the team can quantify divergence and identify bias. This is the systematic method for measuring sensitivity to demographic attributes, and it aligns with trustworthy AI evaluation practices for healthcare deployments.
- ✗
Increase the temperature setting to produce more diverse summaries.
Why it's wrong here
Temperature controls randomness in token sampling, not sensitivity to demographic attributes. Raising it would make outputs less deterministic and harder to compare, but it would not isolate or quantify bias. This approach confounds the analysis and does not provide a systematic measure of demographic sensitivity, so it fails to address the team's goal.
- ✗
Apply TensorRT-LLM INT8 quantization to reduce model variance.
Why it's wrong here
Quantization reduces numerical precision to improve performance, but it does not remove bias or measure demographic sensitivity. It may even introduce additional output variability. The team needs a fairness evaluation method, not an inference optimization, so quantization does not answer the question of how demographic descriptors affect summaries.
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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.