NCA-GENL Experimentation Practice Question
You are experimenting with RAG and notice the model is frequently ignoring the provided context. Which of the following is the most likely culprit to investigate first?
⚠ Common exam trap
Candidates often jump to retraining the model or changing the vector database, ignoring the simpler, more effective fix of adjusting the system instructions to force context adherence.
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
✓
The prompt instruction strength.
LLMs often exhibit 'pre-training bias,' where they prioritize their internal knowledge base over the provided context. If the context is ignored, the retrieval quality or the prompt's instruction is usually insufficient to override this behavior. Experimentation should focus on prompt engineering—specifically strengthening the system instructions to enforce context usage—before attempting more complex architectural changes to the retrieval process or the model itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The GPU clock speed.
Why it's wrong here
GPU clock speeds affect the speed of computation and power consumption, but they have absolutely no impact on the semantic logic of the model's output or the attention mechanism's ability to prioritize provided context. Changing this will not resolve issues with hallucination or context negligence in RAG.
- ✓
The prompt instruction strength.
Why this is correct
The prompt acts as the steering mechanism for the model. If it is too vague, the model defaults to internal knowledge. Strengthening the instruction to explicitly state that the answer must be derived solely from the provided context is the most efficient first step in iterative prompt experimentation.
- ✗
The number of training epochs.
Why it's wrong here
Training epochs are relevant for fine-tuning, not for RAG inference. Even if you were fine-tuning, the number of epochs is a training parameter that does not directly dictate how a model attends to retrieved context during the generation phase of a deployed RAG pipeline application.
- ✗
The total number of parameters in the model.
Why it's wrong here
While larger models may follow instructions better, simply swapping the model is a drastic and expensive experimentation step. It is inefficient to replace the model before verifying that the current model's failure to use context isn't simply a result of poor prompting or retrieval retrieval quality.
About these practice questions
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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.