NCP-GENL Prompt Engineering Practice Question
An engineer is optimizing prompts for an NVIDIA NIM-hosted model used in a multi-turn technical troubleshooting chat. The model forgets earlier constraints, such as the customer's environment and the product version, as the conversation grows. Which prompt engineering technique best preserves these constraints across turns?
⚠ Common exam trap
The trap here is thinking that more conversation history or deterministic sampling will fix forgetting, when the real fix is summarizing and re-prioritizing the constraints each turn.
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
✓
Maintain a compact running summary of key constraints and inject it into the system prompt each turn.
Multi-turn memory is best preserved by extracting and re-injecting critical constraints rather than replaying all dialogue. A compact running summary placed in the system prompt keeps key facts salient and stable while avoiding context bloat. Temperature and single-turn designs do not address memory, and verbatim history can dilute attention and waste tokens.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Repeat the full conversation history verbatim in every turn and increase max_tokens.
Why it's wrong here
Repeating the entire history can preserve details, but it grows token usage quickly and may push important constraints out of the effective context or dilute attention. Increasing max_tokens does not improve memory of earlier turns. This approach is inefficient and can degrade performance as irrelevant dialogue accumulates, rather than reliably preserving the key constraints.
- ✓
Maintain a compact running summary of key constraints and inject it into the system prompt each turn.
Why this is correct
A running summary keeps essential facts such as environment and product version in a stable, high-priority position, so they survive across turns without unbounded token growth. Injecting it into the system prompt gives those constraints consistent influence over every response. This directly addresses forgetting while controlling context length in a multi-turn chat.
- ✗
Use a single-turn prompt for each message and rely on the model's pretrained knowledge of the product.
Why it's wrong here
Treating each message as a single turn discards the conversation state entirely, so the model cannot recall the customer's environment or version. Pretrained knowledge is generic and may not match the specific deployment. This approach guarantees that earlier constraints are lost and increases the chance of irrelevant or incorrect troubleshooting steps.
- ✗
Lower the temperature to 0 and remove the system prompt to avoid conflicting instructions.
Why it's wrong here
Lowering temperature makes responses more deterministic but does nothing to preserve earlier constraints. Removing the system prompt eliminates a prime location for persistent instructions, making the model more likely to forget them. This combination reduces both consistency and the ability to carry forward important context across turns.
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.