NCP-GENL Prompt Engineering Practice Test 2 — 15 Questions

Question 1

Which of the following describes the 'Delimiters' technique in prompt engineering?

Accepted Answer

A technique to protect the model from prompt injection.. Delimiters are characters or sequences (like triple backticks or XML tags) used to clearly separate different parts of the input. This helps the LLM distinguish between instructions, user data, and background context. This is crucial in NVIDIA AI applications that process complex documents or code snippets to prevent the model from confusing control instructions with the data it is supposed to process.

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A way to increase the model's token limit.

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A method to compress text data before sending to the GPU.

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A method for adjusting the model's sampling temperature.

Question 2

When implementing Chain-of-Thought (CoT) prompting for a complex NVIDIA NeMo-based reasoning task, what is the primary benefit of encouraging the model to generate intermediate steps?

Accepted Answer

It decomposes complex problems into verifiable logical segments.. Chain-of-Thought prompting decomposes complex problems into sequential logical steps, which is critical when using LLMs for technical reasoning tasks. By forcing the model to articulate its internal logic, the likelihood of hallucination decreases significantly. This practice is essential for NVIDIA engineers deploying agents that require high-precision output, as it creates an audit trail for the model's reasoning process and allows for better debugging of multi-turn interactions.

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It forces the model to use more GPU memory per token.

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It increases the likelihood of the model selecting a random seed.

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It eliminates the need for system-level instructions entirely.

Question 3

Refer to the exhibit. Given this NeMo configuration, which prompt modification would best improve the reliability of technical support queries?

Accepted Answer

Require the model to state 'I cannot answer this' if the manual is silent.. The exhibit shows a relatively low temperature, which is good for consistency, but the system prompt lacks specific constraints on how to handle missing data. By modifying the prompt to strictly enforce grounding in the manuals and providing a fallback mechanism, you significantly improve the model's reliability in technical support scenarios. This is a critical step for maintaining quality in production environments utilizing NVIDIA NeMo technology.

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Append 'Be as creative as possible' to the system prompt.

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Change the stop sequences to be empty.

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Increase the temperature to 0.9 to ensure varied answers.

Question 4

What is the primary purpose of 'Few-Shot Prompting' in the context of LLM optimization?

Accepted Answer

To provide in-context learning examples to guide output.. Few-shot prompting involves providing a few examples of input-output pairs within the prompt to guide the model's performance on a specific task. This approach helps the model learn the desired format, tone, and logic without the need for intensive fine-tuning. It is a highly efficient way to steer model behavior for specific enterprise use cases, ensuring consistent results across multiple interaction sessions.

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To reduce the latency of the underlying GPU cluster.

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To compress the model weights for deployment.

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To permanently store data in the model's internal memory.

Question 5

Which technique is most appropriate for a task requiring an LLM to generate code in a specific enterprise-internal syntax that is not well-represented in its public training data?

Accepted Answer

Few-shot prompting with multiple code examples.. When dealing with proprietary or rare syntax, few-shot prompting with high-quality, representative examples is the most effective way to guide the model. By including these examples within the prompt, you provide the context the model lacks, significantly reducing syntax errors. This is a critical skill for NVIDIA developers building custom coding assistants for internal proprietary frameworks or legacy infrastructure support.

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Zero-shot prompting with broad general coding instructions.

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Increasing the model temperature to encourage exploration.

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Reducing the context window to force brevity.

Question 6

Refer to the exhibit. Which prompt engineering technique would best force the model to prioritize technical detail over marketing language?

Accepted Answer

Add a constraint: 'Exclude marketing language and include specific architectural metrics like TDP, memory bandwidth, and interconnect speeds.'. The model is failing to adhere to the implicit expectation of 'technical depth.' By explicitly defining a structural constraint—such as requiring specific architectural metrics or removing marketing terminology—the model is forced to prioritize the technical aspects requested. This type of constraint-driven prompting is essential for professional NVIDIA technical writers and engineers who need precise documentation summaries.

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Ask the model to 'Write a shorter summary' in the user prompt.

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Decrease the temperature to 0.0 to make the model more factual.

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Use few-shot prompting with generic summaries.

Question 7

When evaluating an LLM's response to a complex prompt, what is the 'Persona Adoption' technique?

Accepted Answer

A method to assign an expert identity to improve response quality.. Persona adoption involves assigning a specific role, expertise level, or professional identity to the LLM within the system prompt. This technique helps calibrate the tone, vocabulary, and depth of the response to match the user's expectations. For NVIDIA applications, this ensures that the model speaks with the authority and technical precision required for engineering and developer-facing communications.

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A method to verify if the model has memorized personal data.

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A way to force the model to identify the user's persona.

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A technique to reduce the model's context window usage.

Question 8

When building an NVIDIA NeMo LLM application for automated document review, which THREE of the following prompt design choices are critical for ensuring high-quality output? (Select exactly THREE)

Accepted Answer

Use standardized delimiters to isolate user-provided documents.. Effective document review requires accuracy, traceability, and consistency. Using clear delimitation prevents data corruption, requiring structured output (like JSON) allows for downstream programmatic integration, and chain-of-thought prompts ensure the model validates its conclusions against the text. These choices together create a robust, production-ready pipeline that minimizes errors and provides the necessary structure for automated workflows in enterprise environments.

Answer

Include instructions that prioritize speed over accuracy.

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Use a high temperature setting to ensure diverse review perspectives.

Question 9

Which prompt engineering strategy helps the model maintain focus when processing an extremely long document within a single context window?

Accepted Answer

Instruct the model to analyze the document in sections and summarize each.. Long context windows can lead to the 'Lost in the Middle' phenomenon, where the model performs better on the beginning and end of the document but ignores the middle. Using 'attention-focusing' instructions or prompting the model to summarize segments before synthesizing a final answer helps maintain performance across the entire document. This is critical for NVIDIA engineers working with large-scale technical whitepapers and documentation.

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Always increase the model's temperature to 1.0.

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Remove all system-level instructions to save tokens.

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Limit the context window size to 1024 tokens.

Question 10

Refer to the exhibit. What prompt engineering strategy ensures the model consistently maintains its persona and technical expertise throughout this multi-turn dialogue?

Accepted Answer

Use a persistent system prompt that defines the persona and constraints.. In multi-turn conversations, the model can 'drift' away from its original instructions. Re-affirming constraints or using a 'System Prompt' that is explicitly included in the context of every turn is critical. For an NVIDIA code optimizer, the model must maintain its technical persona, specifically focusing on CUDA performance, regardless of how complex the dialogue becomes, ensuring the expertise level remains consistent throughout.

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Append the persona instruction to every user turn.

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Increase the temperature to 1.0 to keep the model 'active'.

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Force the model to provide a summary of its own persona at the end of each turn.

Question 11

Which of the following describes the 'Chain-of-Verification' (CoVe) prompting technique?

Accepted Answer

A process where the model checks its own claims for factual consistency.. Chain-of-Verification is a sophisticated technique designed to reduce hallucinations. It prompts the model to generate a response, then draft questions to verify its own claims, answer those questions, and finally revise the original response based on the verification. This is highly effective for NVIDIA developers building high-stakes applications where factual accuracy is non-negotiable and automated auditing is required.

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A method to verify that the GPU is running at full capacity.

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A technique to optimize the model's weights during training.

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A protocol for securing the prompt against injection attacks.

Question 12

A financial analyst is using an NVIDIA NIM-hosted Llama 3.1 70B model to extract key financial metrics from quarterly earnings call transcripts. The model inconsistently returns a prose summary instead of the required structured JSON. The analyst needs the output to be reliably parseable by a downstream script that expects a fixed schema with fields "revenue", "eps", and "guidance". Which prompt engineering technique is most appropriate to enforce this output format?

Accepted Answer

Provide a JSON schema and a completed example in the prompt, and instruct the model to return only JSON matching that schema.. Enforcing a structured output like JSON requires explicit specification of the schema and often a concrete example. Providing a schema and a completed example leverages the model's in-context learning to mimic the exact format, while the instruction to return only JSON reduces extraneous text. Other techniques like temperature adjustment or chain-of-thought do not directly control output structure and may even worsen format compliance.

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Increase the temperature parameter to 0.9 so the model explores more diverse output formats and may eventually produce JSON.

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Use a chain-of-thought prompt asking the model to 'think step by step' before answering, which will naturally lead to JSON output.

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Add a system prompt that says 'You are a helpful assistant' and rely on the model's instruction-following ability to infer the JSON requirement.

Question 13

A technical support team is building a chatbot using an NVIDIA NIM microservice. The chatbot must answer questions about a specific product's warranty policy. The team wants to ensure the model's responses are grounded in the official warranty document, which is 50 pages long, and avoid inventing policy details. Which prompt engineering approach is most effective for this scenario?

Accepted Answer

Use a retrieval-augmented generation (RAG) pipeline to fetch the most relevant warranty sections and include them in the prompt as context.. Retrieval-augmented generation (RAG) is the most effective way to ground responses in a specific document. It retrieves relevant sections and includes them in the prompt, ensuring the model's answer is based on factual content rather than pre-trained memory. This reduces hallucinations and handles documents that exceed the context window. Other methods either risk hallucination, are inefficient, or lack flexibility.

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Ask the model to 'think step by step' and reason about the warranty policy from its pre-trained knowledge.

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Fine-tune the model on the warranty document using NVIDIA NeMo, then use the fine-tuned model without any additional context in the prompt.

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Include the entire warranty document in the system prompt and instruct the model to answer based only on that document.

Question 14

A developer is building a customer support assistant using an NVIDIA NIM microservice for a Llama 3.1 8B Instruct model. The assistant must answer questions about an order solely based on a JSON payload containing order details, and it must not use any outside knowledge. Which prompt engineering approach best ensures the model adheres to this constraint?

Accepted Answer

Include the JSON payload in the prompt and instruct the model to answer using only the provided data, adding a fallback phrase for missing information.. Grounding the model with the exact JSON payload and instructing it to answer only from that data, with a fallback for missing information, ensures responses are based solely on the provided order details. This leverages the instruction-following ability of Llama 3.1 and avoids reliance on external knowledge. Other methods like fine-tuning or temperature adjustment do not enforce strict data adherence at inference time.

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Prefix the prompt with a system message that says 'You are a helpful assistant' and then ask the question without including the JSON.

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Fine-tune the model on a dataset of order-related questions and answers before deploying it as a NIM microservice.

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Use a low temperature setting (e.g., 0.1) to make the model more deterministic and less likely to hallucinate.

Question 15

An engineer is building a customer support assistant using an NVIDIA NIM for a Llama 3 70B model. The assistant must always respond in valid JSON containing exactly the keys "issue" and "urgency", and must never include any other text. Which prompt engineering approach most directly enforces this output contract?

Accepted Answer

Add a system message that specifies the JSON schema and instructs the model to output only JSON, then validate responses programmatically.. The most direct way to enforce a strict JSON contract is to state the schema and the output-only requirement in the system message, which has the highest priority in the prompt hierarchy, and then validate the result programmatically. Sampling parameters and stylistic instructions do not constrain structure, so they cannot guarantee the two required keys or the absence of extra text.

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Raise the temperature to 1.0 so the model explores more possible response formats and eventually produces valid JSON.

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Append the phrase "Please be concise" to every user message and rely on the model to infer the JSON structure.

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Set the top_p value to 0.1 and the max_tokens parameter to 50 to force the model into a JSON-only response mode.