1Z0-1127 Prompt Engineering — 20 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?

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.

Answer

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

Answer

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 2

A data scientist is designing a prompt to generate a structured report with sections for Summary, Findings, and Recommendations. Which output format specification in the prompt would be MOST effective?

Accepted Answer

"Provide the output in JSON format with keys: 'summary', 'findings', and 'recommendations'.". Specifying JSON output with clear keys ensures the model returns a structured, machine-parseable result. Natural language descriptions are ambiguous, and markdown may not be reliably parsed.

Answer

"Write a report with three sections: Summary, Findings, Recommendations."

Answer

"Return the report in bullet points."

Answer

"Output the report as a markdown document with headings."

Question 3

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.

Answer

Few-shot prompting

Answer

Temperature tuning

Answer

Tree-of-thought prompting

Question 4

Which parameter controls the creativity and randomness of a model's output by adjusting the probability distribution before sampling the next token?

Accepted Answer

Temperature. Temperature scales logits before softmax; higher values increase randomness.

Answer

Frequency penalty

Answer

Max tokens

Answer

Top-k

Question 5

An engineer is using the ReAct pattern to build a reasoning agent. The agent should first reason about the user query, then call an external API, and finally incorporate the API result into a final answer. Which prompt structure best implements this pattern?

Accepted Answer

"You have access to an API. For each step, output 'Thought:', then 'Action:', then 'Observation:' before the final answer.". ReAct explicitly interleaves reasoning (Thought) and actions (Action) before final output.

Answer

"You are a helpful assistant. Answer the user's question based on your knowledge."

Answer

"First, call the API. Then, output the result."

Answer

"Think step by step, then provide the final answer."

Question 6

A prompt engineer is tasked with reducing hallucinations in a document-grounded generation task using Cohere Command R. Which system prompt component is MOST effective for enforcing that the model only uses provided documents?

Accepted Answer

"Answer the question based solely on the provided document. If the document does not contain the answer, say 'I don't know'.". Explicit instruction to base answers solely on provided context, with a constraint to say if information is missing, directly reduces hallucinations.

Answer

"You are an expert in the field. Use your extensive knowledge to answer."

Answer

"Provide a detailed answer with references."

Answer

"Use a temperature of 0.0 for factual answers."

Question 7

In few-shot prompting, what is the primary purpose of including examples in the prompt?

Accepted Answer

To provide a template for the desired output format and reasoning pattern. Examples guide the model on the desired input-output pattern, improving task performance without fine-tuning.

Answer

To reduce the need for a system prompt

Answer

To increase the model's vocabulary

Answer

To decrease the computational cost of inference

Question 8

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.

Answer

Remove the output format specification to give the model freedom

Answer

Increase the temperature to 0.9 for more creative solutions

Answer

Set the max tokens to a very high value

Question 9

Which prompt engineering technique asks the model to explain its reasoning process step-by-step before arriving at the final answer?

Accepted Answer

Chain-of-thought prompting. Chain-of-thought prompting explicitly instructs the model to think step by step, improving reasoning accuracy.

Answer

Tree-of-thought prompting

Answer

Self-consistency

Answer

Zero-shot prompting

Question 10

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.

Answer

Few-shot examples

Answer

User message

Answer

Stop sequences

Question 11

A user repeatedly gets the same phrase output by the model. Which parameter adjustment is MOST likely to reduce such repetitive patterns?

Accepted Answer

Increase frequency penalty. Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly combating repetition.

Answer

Decrease max tokens

Answer

Increase temperature

Answer

Increase top-p

Question 12

A team is prompting a model to translate English to French. They notice the model occasionally mixes French with other Romance languages. Which prompt refinement strategy would MOST likely improve language consistency?

Accepted Answer

Add a constraint in the system prompt: 'Translate only to French. Do not use any other language.'. Explicitly constraining the output to French only, with a clear prohibition against other languages, directly addresses language mixing.

Answer

Use a stop sequence to halt after the first sentence

Answer

Lower top-k to 1 for deterministic output

Answer

Increase temperature to encourage more focused output

Question 13

A prompt engineer is designing a prompt for legal document analysis. The model should output JSON with a specific structure. Which TWO practices are essential to include in the prompt?

Accepted Answer

Define the JSON schema in the system prompt. Option A is correct because defining the JSON schema in the system prompt explicitly instructs the model on the exact structure and data types required for the output. This is a foundational practice in prompt engineering for structured data generation, as it reduces ambiguity and ensures the model adheres to a predefined format, which is critical for downstream parsing in legal document analysis.

Answer

Use a stop sequence of "}"

Answer

Increase max tokens to 5000

Answer

Set temperature to 1.0 for creativity

Question 14

A developer is using Cohere Command R with document-grounded generation. Which THREE elements must be included in the prompt to enable effective document grounding?

Accepted Answer

Preamble that instructs the model to use provided documents. The preamble sets the task, conversation history maintains context, and the document syntax provides source material.

Answer

A stop sequence to end generation

Answer

A temperature of 0.0

Question 15

A prompt engineer is iteratively refining a prompt for a summarization task. Which THREE practices should she adopt to improve prompt performance systematically?

Accepted Answer

Test prompt with a diverse set of input documents. Testing with diverse inputs, establishing evaluation criteria, and A/B testing prompt variants are core to iterative refinement.

Answer

Always use the same input to ensure consistency

Answer

Increase max tokens to the maximum allowed

Question 16

A prompt engineer wants to ensure the model outputs a JSON object with specific keys. Which prompt component is most appropriate to specify this requirement?

Accepted Answer

Output format specification. Output format specification explicitly tells the model the desired structure, such as JSON, XML, or markdown. The other options serve different purposes.

Answer

Task instruction

Answer

Constraints

Answer

Context/background

Question 17

A practitioner is developing a legal document summarization system and needs to reduce hallucinations. Which prompting technique is most effective for improving factual accuracy by exploring multiple reasoning paths?

Accepted Answer

Self-consistency prompting. Self-consistency generates several reasoning chains and aggregates the results, increasing reliability and reducing hallucinations in tasks requiring factual accuracy.

Answer

Few-shot prompting

Answer

Zero-shot prompting

Answer

Chain-of-thought prompting

Question 18

A developer is using the Cohere Command model via OCI Generative AI and wants to ground responses in a specific uploaded document. Which syntax should be used in the preamble to enable document-grounded generation?

Accepted Answer

Use the preamble field with 'document text'. Cohere's document-grounded generation uses a special preamble format that includes document references. The correct syntax is to enclose document text in tags within the preamble.

Answer

Set a system message with 'DOCUMENT: ...'

Answer

Include the document in the user message with 'Document: ...'

Answer

Pass the document as a separate parameter named 'context'

Question 19

Which parameter controls the randomness of the model's output by scaling the probability distribution before sampling?

Accepted Answer

temperature. Temperature scales the logits before softmax, affecting creativity. Higher values increase randomness; lower values make output more deterministic.

Answer

top-k

Answer

frequency_penalty

Answer

top-p

Question 20

A prompt engineer is designing a system prompt for a customer support agent. The goal is to ensure the agent always responds in a polite, professional tone and never provides medical advice. Which approach best achieves this?

Accepted Answer

Use a system prompt that includes both the desired tone and the constraint about medical advice. System prompt is designed to set persona, tone, and constraints. Including both tone and constraints in the system prompt is the intended practice.

Answer

Set the temperature to 0 to ensure consistent tone and hope it avoids medical topics

Answer

Specify tone in the system prompt and add the medical advice restriction in each user message

Answer

Rely on the model's inherent training to avoid medical advice, so no explicit instruction needed