1Z0-1127-25 Prompt Engineering — 15 Questions

Question 1

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

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

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date answers while keeping the underlying LLM static, which directly addresses the requirement of monthly updates without retraining.

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Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

Question 2

A developer notices that a Cohere Command model occasionally generates contradictory statements in the same response when asked to reason step-by-step. Which technique is designed to address inconsistency by generating multiple reasoning paths and selecting the most consistent answer?

Accepted Answer

Self-consistency. Self-consistency is a technique specifically designed to address inconsistency in model outputs by sampling multiple reasoning paths (e.g., via chain-of-thought) and then selecting the most consistent answer through majority voting or similar aggregation. This directly targets the problem of contradictory statements within a single response by leveraging the model's own diverse reasoning trajectories to converge on a reliable answer.

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Few-shot prompting

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Temperature tuning

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Tree-of-thought prompting

Question 3

A developer creates a prompt for a code generation task but the output often contains syntax errors. Which adjustment to the prompt is MOST likely to improve correctness?

Accepted Answer

Add a few examples of correct code for similar tasks in the prompt. Adding few-shot examples of correct code directly in the prompt provides the model with explicit patterns to follow, reducing syntactic drift. This technique, known as few-shot prompting, anchors the model's output to the demonstrated structure and syntax, which is far more effective than relying on the model's internal knowledge alone for code generation tasks.

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Remove the output format specification to give the model freedom

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Increase the temperature to 0.9 for more creative solutions

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Set the max tokens to a very high value

Question 4

A prompt engineer wants the model to adopt a strict, professional tone for a financial report generation task. Which prompt component should be used to set this tone and persona?

Accepted Answer

System prompt. The system prompt is the correct component because it is specifically designed to set the model's behavior, tone, and persona at the beginning of a conversation. In the context of a financial report generation task, a system prompt like 'You are a professional financial analyst. Use a strict, formal tone.' instructs the model to adopt that persona for all subsequent interactions, ensuring consistency across the entire session.

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Few-shot examples

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User message

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Stop sequences

Question 5

In prompt engineering, what is the role of a system prompt?

Accepted Answer

To define the model's persona, tone, and behavioral constraints. The system prompt in prompt engineering defines the model's persona, tone, and behavioral constraints, setting the overarching context for all interactions. This is distinct from user prompts, which carry the current query. By establishing these parameters, the system prompt guides the model's responses consistently across a session, ensuring alignment with desired output characteristics.

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To handle error messages from the API

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To specify the user's current query

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To provide examples of desired outputs

Question 6

A developer is using Cohere Command R in OCI to build a question-answering system over a large set of documents. They want the model to generate answers grounded in the provided documents. Which syntax should they use in the prompt to properly ground the model's responses?

Accepted Answer

Utilize Cohere's document-grounded generation syntax, such as inserting 'document:' markers before each document. Cohere Command R supports a specific document-grounded generation syntax where each document is prefixed with 'document:' markers. This tells the model to base its answers strictly on the provided documents, reducing hallucination and ensuring grounded responses. The syntax is part of Cohere's native prompt engineering guidelines for RAG (Retrieval-Augmented Generation) workflows.

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Use the OCI Generative AI service's 'chat' API with a 'knowledgeBase' parameter

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Embed the documents in the system message and ask the model to use them

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Set the 'grounding' parameter to 'true' in the API call

Question 7

A team is building a code generation assistant that should output Python code without any explanations. They want to ensure the model only returns the code block. Which prompt strategy is MOST effective?

Accepted Answer

Set the system prompt to 'You are a code generator. Output only Python code. Do not include any explanations.' and the user message to 'Generate code for...'. Setting the system prompt to explicitly instruct the model to output only Python code without explanations directly enforces the desired behavior at the highest priority level of the prompt hierarchy. This approach leverages the system prompt's role as a persistent instruction that overrides user messages, ensuring the model strictly returns code blocks without extraneous text.

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Use chain-of-thought prompting to reason about the code before writing it

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Use a zero-shot prompt: 'Write Python code for the following task:'

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Set max tokens to a low value to limit output length

Question 8

Which TWO of the following are common prompt failures?

Accepted Answer

Ambiguous instructions. Ambiguous instructions are a common prompt failure in LLM interactions. When a prompt lacks clarity or specificity, the model cannot reliably determine the user's intent, leading to off-target or inconsistent outputs. This is a fundamental failure mode in prompt engineering, as the model relies entirely on the given text to infer the desired response.

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Providing too many examples

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Using system prompt to set persona

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Specifying output format as JSON

Question 9

A prompt engineer wants the LLM to output a list of countries in a specific JSON format with fields 'country_code' and 'name'. Which prompt component should be used to define this structure?

Accepted Answer

Output format specification. The output format specification is the prompt component explicitly designed to define the structure, schema, or layout of the LLM's response. In this scenario, the prompt engineer needs the LLM to output a list of countries with specific JSON fields ('country_code' and 'name'), which is a direct instruction about the format of the output, not the task itself. This component ensures the LLM adheres to a precise data structure, such as JSON, XML, or a table, which is critical for downstream parsing or integration.

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Constraints

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Context/background

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Task instruction

Question 10

A team is building a legal document summarization system using OCI Generative AI. They need to ensure high accuracy and adherence to strict output formatting. Which THREE practices should they adopt? (Choose three.)

Accepted Answer

Define a system prompt specifying the role of a legal expert and constraints. Defining a system prompt that specifies the role of a legal expert and constraints guides the OCI Generative AI model to adopt a precise, authoritative tone and adhere to domain-specific rules. This is a core prompt engineering technique that sets the context and boundaries for the model's behavior, directly improving accuracy and compliance with legal standards.

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Disable stop sequences to allow complete summaries

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Set the temperature to 0.9 to encourage creative variations

Question 11

Which prompt engineering technique involves providing the model with a few input-output examples within the prompt to guide its response?

Accepted Answer

Few-shot prompting. Few-shot prompting (Option C) is the correct answer because it explicitly involves providing the model with a small set of input-output examples within the prompt to guide its response. This technique helps the model understand the desired task format, style, or reasoning pattern without requiring fine-tuning, leveraging in-context learning to adapt its output based on the provided demonstrations.

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Tree-of-thought prompting

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Zero-shot prompting

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Chain-of-thought prompting

Question 12

A developer is using the Cohere Command model to build a Q&A system that must ground answers in provided documents. Which syntax should be used to indicate the document text in the user message?

Accepted Answer

Enclose the document text in tags within the preamble. In Cohere Command models for document-grounded generation, the document text should be included in the preamble using appropriate tags (e.g., tags). Placing documents in the user message (as in option B) is incorrect; they must be in the preamble to be processed as grounding context.

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Append the document text after a '---' separator in the user message

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Use the <results> tag in the user message to wrap the document text

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Place the document text in the system prompt as a context parameter

Question 13

A prompt engineer notices that the model's responses are frequently repetitive and contain redundant phrases. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty to a positive value, e.g., 0.3. Increasing the frequency penalty (e.g., to 0.3) directly penalizes tokens that have already appeared in the generated text, reducing the likelihood of the model repeating the same phrases. This parameter is specifically designed to discourage repetition by subtracting a fixed amount from the log-probability of each token each time it has been generated, making it the most targeted adjustment for this issue.

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Increase the presence penalty to a positive value, e.g., 0.3

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Decrease the top-p value to 0.5

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Increase the temperature to 1.5

Question 14

A company is building a code generation assistant. They want the model to output only valid JSON and to avoid any natural language explanations. Which TWO settings should be applied?

Accepted Answer

Add a system prompt: "You are a JSON-only code generator. Output only JSON, no explanation.". To ensure the model outputs only valid JSON without natural language explanations, two settings are needed: a system prompt instructing JSON-only output (option B) and a user message explicitly requesting JSON without extra text (option D). These two instructions reinforce the constraint effectively.

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Set max_tokens to a low value to force brevity

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Set temperature to 0.5 for balanced creativity

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Use a stop sequence like '
' to prevent extra lines

Question 15

A prompt engineer wants to use the self-consistency technique to improve answer reliability. Which THREE steps are part of implementing self-consistency?

Accepted Answer

Generate multiple independent responses using chain-of-thought prompting. Self-consistency relies on generating multiple diverse reasoning paths via chain-of-thought (CoT) prompting. This technique samples several independent responses, each following a step-by-step reasoning process, to capture different valid approaches to the same problem.

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Use a single response with high temperature

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Use a stop sequence after each reasoning step