1Z0-1127-25 Practice Test 24 — 15 Questions

Question 1

A data scientist wants to generate a response that lists top-3 product features in a bulleted format with no introductory text. Which prompt engineering technique should be used?

Accepted Answer

Include a clear task instruction specifying the format. Specifying the output format (bulleted list, no intro text) directly in the instruction is the most straightforward way to control the structure. Few-shot examples could also work but are unnecessary when a clear instruction suffices.

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Set temperature to 0 to ensure deterministic output

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Provide few-shot examples of desired output

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Use chain-of-thought prompting

Question 2

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.

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Use a stop sequence to halt after the first sentence

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Lower top-k to 1 for deterministic output

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Increase temperature to encourage more focused output

Question 3

A team wants to create a prompt library for their organization's LLM applications. Which practice is MOST important for maintaining prompt quality over time?

Accepted Answer

Version-control prompts and define evaluation metrics for each prompt variant. Versioning prompts and establishing evaluation criteria are key to tracking changes and ensuring quality. Without versioning, it's impossible to know which version produced which result.

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Store all prompts in a shared document without version history

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Assign a single person to manage all prompts without review

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Keep prompts only in the codebase without separate documentation

Question 4

An AI engineer is designing a prompt that requires the model to solve a complex math problem. They want the model to explore multiple reasoning paths and then aggregate the final answer. Which prompting technique BEST fits this requirement?

Accepted Answer

Self-consistency prompting. Self-consistency generates multiple reasoning paths (e.g., using chain-of-thought) and then takes a majority vote or averages results, which directly matches the need for multiple paths and aggregation.

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Zero-shot prompting with a direct instruction

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Tree-of-thought prompting

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Few-shot prompting with worked examples

Question 5

A data scientist is iteratively refining a prompt for a text classification task. Which TWO practices are essential for systematic prompt improvement?

Accepted Answer

Establishing clear evaluation criteria such as accuracy, F1, or human ratings. A/B testing helps compare prompt variants, and establishing evaluation criteria ensures objective measurement of improvements. The other options are either irrelevant or counterproductive.

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Using the same prompt on all inputs without variation

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Randomly changing words in the prompt without tracking changes

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Increasing temperature to generate more diverse outputs for the same prompt

Question 6

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.

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

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

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

Question 7

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.

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

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"Provide a detailed answer with references."

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"Use a temperature of 0.0 for factual answers."

Question 8

A prompt engineer is iteratively refining a prompt for a product review summarizer. They want to test which prompt version yields the most accurate summaries. Which approach should they use?

Accepted Answer

Conduct an A/B test with a diverse set of inputs and compare outputs against established evaluation criteria. A/B testing involves running both prompt variants on the same inputs and comparing outputs against human-annotated ground truth. This provides quantitative evidence of which prompt performs better.

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Increase temperature to get varied outputs and choose the most common one

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Use a different model for each prompt version

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Run both prompts on a single example and pick the better-looking output

Question 9

A prompt engineer wants the model to adopt a formal and authoritative tone when generating financial reports. Which approach is MOST effective?

Accepted Answer

Set the system prompt to 'You are a financial expert. Always respond with a formal, authoritative tone.'. Setting a system prompt (or preamble) instructs the model on its role, tone, and constraints before the user message. This is the standard way to enforce persona and tone consistently.

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Set the temperature to 0 to ensure deterministic output

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Use few-shot examples of formal responses in the user message

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Include the instruction 'Be formal' in the user message

Question 10

A data scientist is using self-consistency decoding to improve the accuracy of a reasoning task. Which of the following best describes the process?

Accepted Answer

Generate multiple independent reasoning paths and aggregate the final answers by majority voting. Self-consistency generates multiple reasoning paths (e.g., with higher temperature) and selects the most consistent answer by majority vote, improving reliability.

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Generate one chain-of-thought reasoning path and use it as the final answer

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Use a single forward pass with a low temperature setting

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Branch into multiple reasoning trees and prune less promising branches

Question 11

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. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

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Fine-tune a base LLM on the policy documents monthly

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

Question 12

A prompt engineer is developing a multi-step reasoning application using the ReAct pattern. The model sometimes outputs incomplete reasoning or forgets to perform an action. Which improvement to the prompt is MOST likely to address this issue?

Accepted Answer

Include a template pattern in the system prompt that explicitly defines the format for each step. Providing a clear template that structures the reasoning and action steps (e.g., 'Thought: ... Action: ... Observation: ...') helps the model follow the ReAct pattern consistently. Role prompting alone does not enforce the step-by-step structure.

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Add a system message that says 'You are a reasoning agent'

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Use a zero-shot prompt with the instruction 'Reason step by step and then act'

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Increase the temperature to 0.8 to allow more creative reasoning

Question 13

A prompt engineer wants to reduce the model's tendency to generate overly creative or off-topic responses when summarizing financial reports. Which parameter adjustment is MOST appropriate?

Accepted Answer

Decrease temperature to 0.2. Lowering temperature (e.g., to 0.2) makes the model more deterministic and focused, reducing creativity and off-topic responses.

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Increase top-k to 100

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Decrease frequency penalty to 0

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Increase temperature to 1.0

Question 14

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.

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Always use the same input to ensure consistency

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Increase max tokens to the maximum allowed

Question 15

A prompt engineer notices that the model's output frequently repeats the same phrases. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty. Frequency penalty reduces the chance of repeating tokens that have already appeared, directly mitigating repetition. Presence penalty also helps but is broader; frequency penalty is more targeted.

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Increase max tokens

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Decrease top-p to 0.5

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Increase temperature to 0.9