1Z0-1127-25 Prompt Engineering Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

An application uses an LLM to summarize legal documents. The summaries sometimes include hallucinations (details not in the original text). Which prompt engineering technique is MOST effective at reducing hallucinations?

Accepted Answer

Include the full document text in the prompt and instruct the model to base its summary only on that text. Providing the full document context within the prompt (e.g., using a template that includes the document text) grounds the model's response and reduces the chance it invents details. Few-shot examples can also help but are secondary to providing the source.

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Increase the temperature to 0.9 to make the model more cautious

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Use a few-shot prompt with examples of correct summaries

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Set the presence penalty to a high value

Question 3

A prompt engineer is designing a system that generates SQL queries from natural language. The model sometimes produces unsafe queries (e.g., DROP TABLE). Which constraint in the system prompt would BEST mitigate this risk?

Accepted Answer

Include a constraint: 'You may only generate SELECT statements. Do not generate DDL or DML statements like DROP, DELETE, INSERT, or UPDATE.'. It explicitly prohibits the model from generating DDL (Data Definition Language) and DML (Data Manipulation Language) statements, directly addressing the risk of unsafe queries like DROP TABLE. By constraining the output to only SELECT statements, the prompt enforces a strict policy that prevents the model from producing destructive or modifying SQL commands, which is the most effective mitigation among the options.

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Use a few-shot prompt with only safe SELECT examples

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Use role prompting: 'You are an expert SQL developer'

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Set frequency penalty to 1.0 to avoid repetitive unsafe patterns

Question 4

A developer wants the LLM to solve a math problem by reasoning step by step. Which prompting technique should they use?

Accepted Answer

Chain-of-thought prompting. Chain-of-thought prompting explicitly instructs the model to show its reasoning steps, improving accuracy on multi-step problems.

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Zero-shot prompting

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Tree-of-thought prompting

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Few-shot prompting

Question 5

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 6

A company is building a chatbot that must maintain a professional tone and avoid discussing off-topic subjects. Which TWO prompt engineering approaches should they combine to enforce these requirements?

Accepted Answer

Use a system prompt that defines the chatbot's role (e.g., 'You are a professional customer support agent') and includes constraints (e.g., 'Do not discuss topics outside of product support.'). A system prompt with role and constraints sets the overall behavior, and a template pattern for responses provides a consistent structure. The other options are less suitable.

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Use a few-shot prompt with examples of off-topic conversations to teach the model what to avoid

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Set frequency penalty to 2.0 to reduce repetition of any words

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Set temperature to 1.0 to ensure creative responses

Question 7

A prompt engineer is using the self-consistency technique to improve answer reliability. Which TWO steps are essential when implementing self-consistency?

Accepted Answer

Aggregate the outputs (e.g., by majority voting or marginalizing over reasoning steps) to select the most consistent answer. Self-consistency involves two essential steps. First, generate multiple independent reasoning paths by running the prompt several times with a non-zero temperature to ensure diversity (option E). Second, aggregate the outputs (e.g., by majority voting or marginalizing over reasoning steps) to select the most consistent answer (option D). Options D and E are correct. Option B is not essential because chain-of-thought prompting is a separate technique that can be used with self-consistency but is not required for the method.

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Use tree-of-thought to explore all possible reasoning branches

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Use a chain-of-thought prompt to guide the generation of reasoning paths.

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Set temperature to 0 for reproducible outputs

Question 8

A prompt library manager wants to implement version control for prompt templates used across multiple applications. Which THREE practices should they adopt?

Accepted Answer

Use semantic versioning (e.g., v1.2.3) for prompt templates. Storing templates in a version control system, using semantic versioning, and maintaining a changelog are standard practices for prompt version management. Automated testing is good but not version control per se.

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Automatically test prompts on a fixed set of inputs after each change

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Store prompts only in the application's database without history

Question 9

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

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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

Question 10

What is the primary purpose of setting a low temperature (e.g., 0.1) when generating text with an LLM?

Accepted Answer

Make responses more deterministic and focused. Temperature controls randomness; low values make the model deterministic and focused on the most likely tokens, which is ideal for factual tasks.

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Increase creativity and diversity of responses

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Enable the model to use external tools

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Reduce the maximum number of tokens generated

Question 11

A developer is using Cohere Command R+ via OCI Generative AI and wants to ground answers in a provided set of documents using the `documents` parameter. Which prompt structure ensures the model correctly adheres to the documents?

Accepted Answer

Use a preamble: 'You answer questions based only on the provided documents. If the answer is not in the documents, say you don't know.' Then pass documents via the `documents` parameter in the request. Cohere's document-grounded generation requires the preamble to instruct the model to answer based solely on the documents, and the conversation history must include the documents in the correct format.

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Set a system prompt that says 'You are a helpful assistant' and pass documents in the `documents` parameter with no further instruction

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Place the documents in the conversation history as an assistant message before the user's question

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Include the documents in the user message after the question

Question 12

An AI engineer is designing a prompt to generate a report summary. The prompt currently says: 'Summarize the following text.' The output is often too verbose. Which modification would best enforce a concise, bullet-list format?

Accepted Answer

Change the prompt to: 'Summarize the following text in a bullet list of at most 5 items. Each bullet must be under 10 words.'. Explicitly specifying the output format (bullet list, max 5 items) gives the model clear constraints, reducing verbosity.

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Set temperature to 1.0 for more focused outputs

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Increase the frequency penalty to 2.0

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Add an example summary at the end of the prompt

Question 13

Which prompting technique involves providing the model with a small set of input-output examples within the prompt to guide its behavior?

Accepted Answer

Few-shot prompting. Few-shot prompting includes examples of desired input-output pairs, helping the model infer the task without fine-tuning.

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Tree-of-thought prompting

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Chain-of-thought prompting

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Zero-shot prompting

Question 14

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 15

A prompt engineer wants the LLM to adopt the persona of a 'friendly customer support agent' for all interactions. Which approach is most effective?

Accepted Answer

Set the persona in the system prompt (or preamble) before the conversation begins. Oracle's generative AI services support a system prompt (or preamble in Cohere) that sets the assistant's persona, which applies to the entire conversation.

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Include the persona instruction in every user message

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Use a high temperature to encourage friendly language

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Fine-tune the model on customer support dialogues