NCP-GENL Prompt Engineering — 30 Questions

Question 1

A developer is building a technical support bot that must analyze complex log files to identify root causes. The initial zero-shot prompt often yields generic summaries that miss specific error codes. Which strategy should the developer implement to ensure the model performs logical deduction step-by-step before providing a final answer?

Choose an answer to begin — your selection is scored in the full session.

Answer

Temperature reduction to zero

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Chain-of-Thought (CoT) prompting

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Increasing the frequency penalty

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Few-shot prompting with summaries

Question 2

When designing a prompt for a high-stakes financial application, which TWO techniques are most effective for mitigating the risk of prompt injection and ensuring the model adheres to its system-defined guardrails?

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Using distinct delimiters for user input

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Implementing recursive character splitting

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Placing user input before the system instructions

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Utilizing a robust system-level prompt

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Applying a high Top-p value

Question 3

An AI practitioner wants to generate a list of creative marketing slogans for a new product but finds the output too repetitive and predictable. Which hyperparameter should be adjusted to increase the randomness and diversity of the slogans generated by the prompt?

Answer

Presence penalty

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Temperature

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Max tokens

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Stop sequences

Question 4

A data scientist is using few-shot prompting to classify customer feedback into five distinct categories. Which THREE factors are most critical for selecting effective examples to include in the prompt?

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Label distribution balance

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Example diversity and coverage

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Semantic similarity to the target input

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Alphabetical ordering of examples

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Total character count of all examples

Question 5

Which prompting technique involves the model generating multiple different reasoning paths and then selecting the most common answer among them to improve the accuracy of complex math problems?

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Least-to-most prompting

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Self-consistency prompting

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Tree-of-Thought prompting

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ReAct prompting

Question 6

A developer wants to implement a 'Persona' or 'Role-playing' prompt to improve a customer service bot. Which TWO benefits does this technique provide for the model's output?

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Consistent tone and stylistic alignment

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Reduction in total inference latency

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Implicit context for domain-specific jargon

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Elimination of the need for stop sequences

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Automatic verification of factual accuracy

Question 7

Which component of a prompt is primarily responsible for providing the model with the specific information it needs to answer a query, such as a retrieved document or a set of database records?

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The Instruction

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The Context

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The Output Indicator

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The Few-shot Examples

Question 8

When utilizing the ReAct (Reason + Act) prompting framework, what is the primary purpose of the 'Observation' step in the model's iterative loop?

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It generates the final answer for the user

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It provides the model with the result of an action

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It defines the persona for the reasoning trace

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It is used to penalize repetitive reasoning paths

Question 9

An engineer is struggling with 'hallucinations' in a model that summarizes legal documents. Which TWO prompt engineering adjustments are most likely to reduce the occurrence of fabricated information?

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Instructing the model to say 'I don't know' if unsure

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Adding a requirement for direct quotes and citations

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Increasing the Top-k parameter to 100

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Removing all few-shot examples from the prompt

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Using a very high temperature setting

Question 10

Which prompting strategy is specifically designed to solve problems by first asking the model to break down a complex task into smaller, manageable sub-problems and then solving each one in order?

Answer

Chain-of-Thought prompting

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Least-to-most prompting

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Zero-shot prompting

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Negative prompting

Question 11

A developer is using an LLM to generate SQL queries from natural language. The model frequently confuses table names and column relationships. Which prompt engineering technique would most effectively ground the model's output in the specific database schema being used?

Answer

Increasing the max token limit

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Adding a 'think step-by-step' instruction

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Including the DDL (Data Definition Language) in the context

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Using a more creative persona for the model

Question 12

Which TWO of the following are primary reasons for using delimiters (such as ### or ---) in a prompt that includes both instructions and a long source document?

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To help the model distinguish between instructions and data

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To reduce the total number of tokens used in the prompt

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To prevent the model from following commands inside the data

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To bypass the model's safety and content filters

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To automatically translate the prompt into multiple languages

Question 13

A user wants an LLM to rewrite a paragraph in a 'professional and concise' style. The model provides a professional version, but it is still quite long. Which change to the prompt is most likely to achieve the desired result?

Answer

Changing the temperature to 1.0

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Using a negative prompt like 'Do not use many words'

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Providing a few-shot example of a concise rewrite

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Repeating the word 'concise' five times in the prompt

Question 14

Which TWO of the following strategies effectively reduce hallucination in RAG-based NVIDIA NeMo applications? (Select exactly TWO)

Accepted Answer

Require the model to cite the specific document ID for every claim.. Reducing hallucinations in Retrieval-Augmented Generation requires a two-pronged approach: restricting the model to the provided context and encouraging the model to acknowledge limitations. By explicitly instructing the model to rely only on provided documents and allowing for an 'I don't know' response, the reliability of the system increases. These techniques are standard best practices for maintaining high accuracy in enterprise-grade NVIDIA AI deployments.

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Instruct the model to ignore context if it already knows the answer.

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Set the sampling temperature to 1.5 to maximize creativity.

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Increase the number of context documents to over 100 for every query.

Question 15

When designing prompts for NVIDIA NeMo Guardrails, which THREE of the following are best practices to ensure alignment and safety? (Select exactly THREE)

Accepted Answer

Define clear negative constraints for off-topic interactions.. NeMo Guardrails work best when instructions are explicit, negative constraints are clearly defined, and topic boundaries are strictly enforced. By defining what the model should not do, providing clear examples of off-topic queries, and maintaining consistent persona instructions, developers can create a robust safety layer. These practices ensure the model stays within the intended operational domain while providing consistent and helpful assistance.

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Use highly complex, ambiguous language to test robustness.

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Encourage the model to ignore user input if it sounds frustrated.

Question 16

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 17

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 18

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 19

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 20

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.