1Z0-1127-25 Prompt Engineering Practice Test 2 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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. 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 10

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 11

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 12

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 13

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 14

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 15

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