1Z0-1127-25 Prompt Engineering Practice Test 7 — 15 Questions

Question 1

A developer is tuning parameters for a text-generation model and wants to reduce the likelihood of the same phrase appearing repeatedly. Which parameter should be adjusted?

Accepted Answer

Frequency penalty. Frequency penalty reduces the model's tendency to repeat tokens or phrases by penalizing tokens that have already appeared in the generated text.

Answer

Top-k

Answer

Stop sequences

Answer

Presence penalty

Question 2

What is the primary goal of chain-of-thought prompting?

Accepted Answer

Encourage step-by-step reasoning to improve accuracy on multi-step problems. Chain-of-thought encourages the model to perform step-by-step reasoning before arriving at an answer, improving performance on complex tasks.

Answer

Prevent the model from hallucinating

Answer

Make the output more creative

Answer

Reduce output token count

Question 3

A developer notices that an LLM occasionally generates harmful or biased responses despite a system prompt instructing it to be safe. Which technique can help mitigate this at inference time without retraining?

Accepted Answer

Add a detailed system prompt with explicit safety constraints and use content filtering if available. Using a strong system prompt with explicit constraints is the first line of defense; also, setting low temperature can reduce unpredictable outputs. But among the options, updating the system prompt with more specific guidelines is the most direct approach.

Answer

Increase the top-p value to 0.95

Answer

Use a higher temperature to encourage safer outputs

Answer

Fine-tune the model on a curated safe dataset

Question 4

A prompt engineer is designing a ReAct pattern prompt to enable an LLM to use external tools. Which of the following is a key characteristic of the ReAct pattern?

Accepted Answer

The model produces a chain of thought followed by an action, then observes the result and continues reasoning. ReAct interleaves reasoning steps (e.g., 'I need to find the current weather') with actions (e.g., 'Action: call weather API') and observations, allowing the model to reason and act in a cycle.

Answer

The model uses a single tool call to answer the question

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The model generates a final answer without any intermediate steps

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The model is fine-tuned specifically for tool use

Question 5

An organization wants to ensure that prompts submitted to an LLM do not contain sensitive customer data. Which practice is most effective?

Accepted Answer

Sanitize user inputs by removing sensitive information before including them in the prompt. Sanitizing prompts before submission (e.g., removing PII, using placeholders) prevents sensitive data from being sent to the model. Other options either do not prevent data leakage or are less direct.

Answer

Use a low temperature to avoid generating sensitive data

Answer

Increase the max tokens to allow the model to ignore sensitive data

Answer

Implement a prompt injection detection system that blocks malicious prompts

Question 6

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.

Answer

Using the same prompt on all inputs without variation

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Randomly changing words in the prompt without tracking changes

Answer

Increasing temperature to generate more diverse outputs for the same prompt

Question 7

A developer is using tree-of-thought prompting to solve a complex planning problem. Which THREE characteristics are typical of tree-of-thought?

Accepted Answer

It explores multiple reasoning branches in parallel. Tree-of-thought explores multiple reasoning branches in parallel, uses evaluation or heuristics to prune unpromising branches, and can backtrack to explore alternatives. It does not follow a single linear path or guarantee a single answer without voting.

Answer

It generates a single linear chain of reasoning

Answer

It always returns a single final answer via majority vote

Question 8

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.

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 9

When using Cohere Command models on OCI Generative AI, a developer wants to ground a response based on a provided document. Which syntax should be used to embed document content in the prompt?

Accepted Answer

document: ... <end_document>. Cohere Command models support a special document-grounded generation syntax using the 'document:' prefix. This tells the model to treat the following text as a reference document for grounding.

Answer

{{document}} ... {{/document}}

Answer

[DOC] ... [/DOC]

Answer

<|document|> ... <|/document|>

Question 10

A developer is using chain-of-thought prompting to solve a multi-step math problem. The model produces an incorrect final answer, but the intermediate reasoning steps appear logical. Which technique should be applied to improve accuracy?

Accepted Answer

Use self-consistency by generating multiple reasoning chains and picking the majority answer. Self-consistency generates multiple reasoning paths (using a higher temperature) and then selects the most common final answer. This reduces the chance that a single flawed path leads to an incorrect result.

Answer

Reduce the max_tokens parameter so the model does not over-reason

Answer

Switch to zero-shot prompting to avoid reasoning errors

Answer

Increase the temperature to 1.5 to encourage more diverse reasoning

Question 11

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.

Answer

Set the temperature to 0 to ensure deterministic output

Answer

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

Answer

Include the instruction 'Be formal' in the user message

Question 12

A developer is using a prompt template that includes placeholders like {context} and {question}. They want to version these templates for A/B testing. Which practice is BEST for managing prompt templates?

Accepted Answer

Use a dedicated prompt library with versioning, such as a database table with version numbers. Storing prompt templates in a centralized prompt library with version control enables systematic management, collaboration, and rollback. It also supports A/B testing different versions.

Answer

Save each template as a separate Python file in a Git repository

Answer

Store templates only in the application code as string constants

Answer

Use a spreadsheet to track template versions

Question 13

During iterative prompt refinement, a team evaluates two prompt variants on 100 test queries. Variant A scores 85% accuracy but occasionally generates offensive content. Variant B scores 80% accuracy with no safety issues. Which evaluation criterion should take priority for a customer-facing application?

Accepted Answer

Safety — offensive content is unacceptable in a customer-facing system. For customer-facing applications, safety is paramount. Even if accuracy is slightly lower, ensuring no offensive content is critical to avoid reputational and legal risks. The team should prioritize safety and then work to improve accuracy.

Answer

Accuracy — because it is highest and the offensive content can be filtered post-hoc

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Cost — the variant with higher accuracy uses fewer tokens

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Latency — because the variant with higher accuracy also has lower latency

Question 14

A prompt engineer wants to generate a poem in a specific rhyming scheme (ABAB). Which combination of prompt components is LEAST likely to succeed?

Accepted Answer

Only task instruction: 'Write a poem'. Providing only a task instruction ('Write a poem') without specifying the rhyme scheme or giving examples leaves the model to choose its own structure. The other options give explicit formatting or examples.

Answer

Task instruction: 'Write a poem' and output format specification: 'Use ABAB rhyme scheme'

Answer

Task instruction: 'Write a poem in ABAB rhyme scheme' and an example stanza

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Task instruction: 'Write a poem' with a few-shot example of an ABAB poem

Question 15

Which parameter controls the randomness of the model's output by adjusting the probability distribution of the next token?

Accepted Answer

Temperature. Temperature adjusts the logits before softmax, making lower values (e.g., 0.1) more deterministic and higher values (e.g., 0.9) more random.

Answer

Frequency penalty

Answer

Top-p

Answer

Max tokens