NCP-GENL Prompt Engineering Practice Question
A team is using an NVIDIA NIM for a Mistral model to classify support tickets into one of five fixed categories. Accuracy is inconsistent, and the model sometimes invents new categories. Which prompt engineering change is most likely to improve reliability without retraining the model?
⚠ Common exam trap
The trap here is believing that lowering temperature guarantees correct classification, when the real issue is that the allowed labels were never defined in the prompt.
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
✓
Provide three labeled examples per category in the prompt and instruct the model to output only the category name.
Few-shot examples with explicit labels define the allowed output space, and the instruction to emit only the category name prevents invented labels. This combination directly targets both symptoms, inconsistency and hallucinated categories, and requires no model retraining, unlike sampling or reasoning changes that leave the label set undefined.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a chain-of-thought instruction that asks the model to reason step by step before naming a category.
Why it's wrong here
Chain-of-thought can improve complex reasoning, but for a simple five-way classification it adds tokens and latency without guaranteeing adherence to the label set. The model may still output a novel category at the end of its reasoning. The primary defect is the unconstrained output space, which reasoning alone does not close.
- ✗
Increase the context window by concatenating the entire ticket history for every request.
Why it's wrong here
Adding full ticket history increases input length and may introduce irrelevant details that distract the classifier. It does not constrain the label space, so the model can still invent categories. The core problem is undefined output labels, and more context does not fix that, especially when the extra text contains tangential discussion.
- ✓
Provide three labeled examples per category in the prompt and instruct the model to output only the category name.
Why this is correct
Few-shot examples define the exact label set and demonstrate the mapping from ticket text to category. Instructing the model to output only the category name removes room for invented labels. This directly addresses the inconsistency and the hallucinated categories without any fine-tuning, making it the most effective change for a fixed-label classification task.
- ✗
Lower the temperature to 0.0 and remove all instructions so the model relies on its pretrained knowledge.
Why it's wrong here
Lowering temperature reduces randomness but does not tell the model which five categories exist. Removing instructions makes the task ambiguous, so the model may still produce arbitrary labels. Deterministic sampling of an under-specified prompt yields consistently wrong or invented categories rather than reliable classification.
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 NCP-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 NCP-GENL exam.