1Z0-1127-25 Prompt Engineering Practice Test 8 — 15 Questions

Question 1

A prompt engineer is designing a system that must extract structured data from unstructured text. The model occasionally outputs extra text beyond the required JSON. Which parameter should be adjusted to enforce strict output format?

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

Accepted Answer

Set a stop sequence to the closing delimiter of the JSON (e.g., '}'). Stop sequences tell the model when to stop generating. By adding a stop sequence like '}' (end of JSON), the model will terminate after the JSON object, preventing extra text.

Answer

Increase the frequency penalty

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Reduce the temperature to 0

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Increase the top-p value

Question 2

A developer is using the ReAct pattern to build a reasoning agent. Which of the following best describes the two main steps in this pattern?

Accepted Answer

Reason and Act. ReAct stands for Reason + Act. The model first reasons about the question, then decides on an action (e.g., a tool call) to gather information, iterating until a final answer is reached.

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Retrieve and Generate

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Refine and Aggregate

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Generate and Evaluate

Question 3

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

Accepted Answer

Few-shot prompting. Few-shot prompting includes several examples of the desired input-output mapping to help the model understand the task and output format.

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Chain-of-thought prompting

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Zero-shot prompting

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Tree-of-thought prompting

Question 4

A prompt engineer is refining a prompt that currently produces outputs with excessive repetition. Which TWO parameters can be adjusted to reduce repetition? (Choose two.)

Accepted Answer

Frequency penalty. Frequency penalty discourages tokens that have already appeared, and presence penalty discourages tokens that have appeared at all. Both reduce repetition. Temperature and max tokens do not directly target repetition.

Answer

Temperature

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Max tokens

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Top-p

Question 5

A prompt engineer is testing a new prompt for a Q&A system. The prompt includes ambiguous wording that causes the model to answer inconsistently. Which TWO steps should the engineer take to resolve this? (Choose two.)

Accepted Answer

Test the prompt with a diverse set of inputs to identify remaining ambiguities. Clarifying the prompt removes ambiguity. Testing with diverse inputs helps uncover edge cases. A/B testing would compare variants but does not directly fix ambiguity. Temperature reduction does not clarify instructions.

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Use A/B testing between the original and rewritten prompt to measure improvement

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Increase the temperature to encourage diverse responses

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Reduce the max tokens to force shorter answers

Question 6

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 by retrieving relevant chunks from the policy documents stored in a vector store at inference time, without requiring model retraining. This decouples the knowledge base from the model weights, enabling monthly document updates by simply re-indexing the vector store, which is far more cost-effective and faster than fine-tuning or retraining.

Answer

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 7

When tuning the temperature parameter for a text generation task, which effect does setting temperature to 0.1 have compared to 0.9?

Accepted Answer

It makes outputs more focused and deterministic. Low temperature makes output more deterministic and repetitive; high temperature increases randomness and creativity.

Answer

It increases the maximum number of tokens generated

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It reduces the vocabulary considered at each step

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It increases randomness, producing more diverse outputs

Question 8

A developer is using the Cohere Command R model with a preamble set to "You are a helpful assistant." The user message contains a question. The model's response is factual but lacks detail. To encourage more comprehensive answers, which modification should be made?

Accepted Answer

Add "Provide a thorough and detailed response." to the preamble. Adding a directive in the preamble, such as "Provide thorough and detailed responses," sets the desired behavior from the start.

Answer

Use a stop sequence like "
" to force longer responses

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Set the temperature to 0.0

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Increase the max_tokens parameter to 1000

Question 9

A prompt engineer wants to use chain-of-thought prompting to improve arithmetic reasoning. Which TWO steps are essential for implementing this technique?

Accepted Answer

Provide a few-shot example that includes step-by-step reasoning. Chain-of-thought requires the model to output intermediate reasoning steps, and providing an example with reasoning helps guide the model.

Answer

Add a stop sequence after the final answer

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Use a large max_tokens value to allow long output

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Set temperature to 0.0 for deterministic reasoning

Question 10

Which TWO parameters directly control the randomness and diversity of generated tokens?

Accepted Answer

Temperature. Temperature and top-p (nucleus sampling) are the primary parameters that influence randomness and diversity.

Answer

Stop sequences

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Frequency penalty

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Max tokens

Question 11

A prompt engineer is using the ReAct pattern to enable the model to reason and act (e.g., call tools). Which THREE components are essential in the prompt to implement ReAct correctly?

Accepted Answer

A list of available actions or tools with descriptions. ReAct requires a scratchpad to record reasoning, a set of available actions (tools), and a format showing how to interleave reasoning and actions.

Answer

A stop sequence after each action

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A system prompt that sets a high temperature

Question 12

Which THREE are common prompt failures that can degrade model performance?

Accepted Answer

Prompt injection vulnerabilities that allow malicious input to override instructions. Ambiguous instructions, insufficient context, and prompt injection are well-known failure modes.

Answer

Using too few tokens in the output

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Setting temperature too high

Question 13

A team is iteratively refining a prompt for a summarization task. Which THREE activities are essential for effective iterative prompt refinement?

Accepted Answer

Establish evaluation criteria (e.g., accuracy, coherence, conciseness). Testing with diverse inputs, A/B testing variants, and establishing evaluation criteria are key to systematic refinement.

Answer

Test the prompt with a static set of examples only

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Increase max_tokens gradually

Question 14

Which TWO are benefits of using few-shot prompting compared to zero-shot prompting?

Accepted Answer

It helps the model understand the desired pattern, especially for uncommon tasks. Few-shot provides examples that improve format adherence and guide the model, especially for complex tasks.

Answer

It always reduces the need for parameter tuning

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It eliminates the need for a system prompt

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It reduces the number of tokens in the output

Question 15

Which TWO are best practices for prompt management in production environments?

Accepted Answer

Maintain a prompt library with reusable templates. Versioning and maintaining a library of templates are essential for tracking changes and reusability.

Answer

Avoid using system prompts to keep prompts simple

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Keep all prompts as hard-coded strings in the application code

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Use the same prompt for all use cases to reduce complexity