NCA-GENL Trustworthy AI Practice Question
Why is 'Data Provenance' considered a crucial component in maintaining Trustworthy AI?
⚠ Common exam trap
Candidates often confuse data provenance with model performance metrics or bias evaluation, missing that provenance strictly focuses on tracking the origin, history, legal compliance, and chain of custody of the training data.
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
✓
It provides a clear audit trail for data lineage, ethics, and legal compliance.
Data provenance involves tracking the origin, history, and licensing status of the training data. For Trustworthy AI, it ensures legal compliance, intellectual property rights, and the ability to audit the training set for bias. Without a clear chain of custody for the data, organizations cannot guarantee that their models are trained on ethical, high-quality, and legally obtained information, leading to significant reputation and legal risks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It ensures that the model can be compressed into a smaller size for edge deployment.
Why it's wrong here
Data provenance is about the history and lineage of the training data, not the compression or quantization techniques used to optimize models for edge devices. These are unrelated technical domains; data provenance supports ethics and auditing, while model compression supports resource efficiency and deployment on constrained hardware.
- ✓
It provides a clear audit trail for data lineage, ethics, and legal compliance.
Why this is correct
Provenance is essential for verifying that the model was trained on data that is both legally sourced and ethically managed. It allows organizations to demonstrate compliance during audits and proactively address potential issues related to copyright infringement or data contamination, which are vital for long-term AI sustainability.
- ✗
It speeds up the GPU training process by indexing the data in a vector database.
Why it's wrong here
Data indexing is an engineering optimization for retrieval speed, not a mechanism for tracking the historical provenance or legal status of the data. While indexing helps in RAG systems, it does not provide the historical metadata required for provenance or compliance verification in the context of Trustworthy AI.
- ✗
It automatically corrects grammatical errors in the training corpus.
Why it's wrong here
This describes data cleaning or pre-processing. Provenance is a descriptive and documentary practice, not an active data-modification process. It does not analyze the content of the data for quality or correctness; it analyzes the history of the data to ensure it meets governance and legal requirements.
About these practice questions
One of 367 original NCA-GENL practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.