NCA-GENL Trustworthy AI Practice Question
A hospital's AI governance committee is reviewing a generative model that drafts discharge summaries. They require a documented, auditable record showing which source documents, consent forms, and preprocessing steps produced each training example. Which Trustworthy AI practice does this requirement describe?
⚠ Common exam trap
It's easy for candidates to confuse privacy-preserving training techniques with provenance documentation, since both appear in trustworthy-AI discussions but only one produces an auditable data trail.
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
✓
Data lineage tracking
The committee wants to trace each training example back to its source documents, consent forms, and preprocessing operations, which is precisely what data lineage tracking captures and preserves for audit. Privacy-enhancing techniques such as differential privacy or federated learning change how data is used but do not document its origin, and quantization is purely an inference optimization. Only lineage tracking yields the traceable, reviewable record the governance process demands.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Federated learning
Why it's wrong here
Federated learning trains across decentralized sites without centralizing raw records, which can reduce data movement risk. It still does not inherently generate an auditable map from source document and consent form to each training example. The scenario asks for documentation of the data's journey, not for a distributed training topology, so this technique misses the requirement.
- ✓
Data lineage tracking
Why this is correct
Data lineage tracking records the origin, transformations, and movement of each data element through the pipeline, producing exactly the auditable chain the committee demands from source document to training example. It answers where data came from, what was done to it, and who touched it, which is the practice that satisfies a documented provenance requirement for regulated healthcare content.
- ✗
Differential privacy
Why it's wrong here
Differential privacy adds calibrated noise so that individual records cannot be inferred from model outputs, protecting patient privacy during training. It does not, however, produce a record of which consent forms or preprocessing steps generated a given example. The committee asked for an auditable trail of data origin and transformation, which differential privacy does not supply.
- ✗
Model quantization
Why it's wrong here
Quantization reduces numeric precision of weights and activations to shrink memory footprint and speed inference. It is a performance optimization with no bearing on documenting where training data originated or what consent covered it. Choosing it here would leave the governance committee without the provenance evidence they explicitly require for the discharge-summary model.
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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.