NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A financial services company is deploying an LLM-based assistant that must answer questions about internal compliance documents. The documents are updated weekly, and the company cannot retrain the model every week. The assistant must cite the exact source passage for each answer. Which architecture best satisfies these requirements?
⚠ Common exam trap
The trap here is assuming that fine-tuning is always the best way to add domain knowledge, when the requirements for weekly updates and exact citations point to retrieval-augmented generation instead.
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
✓
Use retrieval-augmented generation (RAG) with a vector index over the compliance documents and return retrieved passages with the answer
RAG separates knowledge from model weights: documents are indexed in a vector store, relevant passages are retrieved at query time, and the LLM generates an answer grounded in those passages with citations. Weekly document updates require only re-indexing, not retraining. Fine-tuning, stuffing all documents into the prompt, or static FAQ routing either conflicts with the update frequency, does not scale, or cannot provide reliable source citations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use retrieval-augmented generation (RAG) with a vector index over the compliance documents and return retrieved passages with the answer
Why this is correct
RAG retrieves relevant passages from an up-to-date vector index at inference time and can include those passages as citations. When documents change, the index is updated without retraining the model. This directly meets the requirements for freshness and source attribution while keeping the LLM's weights fixed, making it the most suitable architecture for this scenario.
- ✗
Train a separate classifier to route questions to static FAQ answers written by the compliance team
Why it's wrong here
A classifier over static FAQs cannot handle novel questions or documents that change weekly without manual rewriting. It also does not generate answers with citations from source passages. This approach limits coverage and freshness, and it does not leverage the LLM's generative ability, so it fails the requirement for accurate, cited answers from updated compliance documents.
- ✗
Fine-tune the LLM weekly on the updated documents and rely on its parametric memory to answer
Why it's wrong here
Weekly fine-tuning is expensive and operationally slow, and it still does not guarantee that the model can cite exact source passages. Parametric memory can blend or distort facts and cannot provide verifiable citations. The scenario explicitly rules out frequent retraining, so this approach conflicts with the stated constraint even if it improved domain knowledge.
- ✗
Increase the model's context window and paste all compliance documents into every prompt
Why it's wrong here
Pasting all documents into every prompt is limited by the context window and becomes impractical as the document set grows. It also increases latency and cost, and the model may still ignore or misattribute information. While it provides raw text, it does not scale to weekly updates or guarantee that the correct passage is cited for each answer.
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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.