1Z0-1127-25 Prompt Engineering Practice Test 3 — 15 Questions

Question 1

A prompt engineer is designing a system prompt for a customer support agent. The goal is to ensure the agent always responds in a polite, professional tone and never provides medical advice. Which approach best achieves this?

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

Accepted Answer

Use a system prompt that includes both the desired tone and the constraint about medical advice. System prompt is designed to set persona, tone, and constraints. Including both tone and constraints in the system prompt is the intended practice.

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Set the temperature to 0 to ensure consistent tone and hope it avoids medical topics

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Specify tone in the system prompt and add the medical advice restriction in each user message

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Rely on the model's inherent training to avoid medical advice, so no explicit instruction needed

Question 2

An AI application uses chain-of-thought prompting to solve math problems. The team observes that the model sometimes arrives at correct answers but with flawed reasoning. Which technique can specifically help by aggregating multiple reasoning paths?

Accepted Answer

Self-consistency prompting. Self-consistency generates several reasoning chains and selects the most common answer, improving robustness even if some chains have flawed steps.

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Role prompting

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Tree-of-thought prompting

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Few-shot prompting

Question 3

Which prompt pattern is most suitable when a user needs to generate a structured report following a specific template every time?

Accepted Answer

Template pattern. Template patterns provide a fixed structure (e.g., fill-in-the-blanks) that the model follows, ensuring consistent output format.

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Recipe pattern

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ReAct pattern

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Role prompting

Question 4

A prompt engineer is testing different versions of a prompt to improve accuracy on a classification task. Which practice is most appropriate for systematic refinement?

Accepted Answer

Run A/B tests on a representative evaluation set and measure accuracy. A/B testing with a holdout evaluation set allows objective comparison of prompt variants and measures performance improvements reliably.

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Always increase the number of few-shot examples

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Manually review outputs of a few examples and adjust based on intuition

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Change the model to a larger one

Question 5

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

Accepted Answer

Few-shot prompting. Few-shot prompting includes several demonstrations of the desired task, helping the model understand the expected output format and reasoning.

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Zero-shot prompting

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Self-consistency prompting

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Chain-of-thought prompting

Question 6

A prompt engineer notices that the model sometimes generates outputs that include parts of the system prompt or user message verbatim. This is likely a symptom of which common prompt failure?

Accepted Answer

Prompt injection vulnerabilities. Prompt injection vulnerabilities can cause the model to leak or repeat the prompt itself. This is a known failure mode where the model confuses the input with output.

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Ambiguous instructions

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Conflicting requirements

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Insufficient context

Question 7

In OCI Generative AI, when using the Cohere Command model, which parameter is used to discourage the model from repeating the same phrases?

Accepted Answer

frequency_penalty. Frequency penalty reduces the likelihood of tokens that have already appeared, directly targeting repetition of phrases.

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presence_penalty

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temperature

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top-k

Question 8

A developer wants the model to generate a bullet list of steps to troubleshoot a network issue. Which output format specification should be included in the prompt?

Accepted Answer

Request 'bullet list' in the output format. Explicitly requesting a bullet list in the output format specification is the most direct way to achieve the desired structure.

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Use a few-shot example with numbered steps

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Provide a table format

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Set temperature to 0

Question 9

A data scientist is designing a prompt for code generation and needs to reduce the likelihood of the model generating incorrect or hallucinated code. Which two parameter adjustments are most effective? (Choose two.)

Accepted Answer

Set frequency_penalty to a moderate value (e.g., 0.5). Lowering temperature reduces randomness, making outputs more deterministic and less prone to hallucinations. Frequency penalty reduces repetitive mistakes. Top-k and presence penalty are less directly effective.

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Set top-k to a high value (e.g., 100)

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Increase max_tokens significantly

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Set presence_penalty to 0

Question 10

An organization maintains a library of prompt templates for various use cases. Which three practices are essential for effective prompt management? (Choose three.)

Accepted Answer

Store prompt templates in a shared, accessible repository. Versioning, storing templates in a central library, and establishing evaluation criteria are key to managing prompts. A/B testing is good but not a management practice; parameter tuning is separate.

Answer

Adjust temperature and top-p for each prompt variant

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Conduct A/B testing on every prompt change

Question 11

A prompt engineer is troubleshooting a chatbot that consistently fails to follow instructions when the user includes adversarial input. Which two strategies can mitigate prompt injection attacks? (Choose two.)

Accepted Answer

Use instruction shielding: clearly separate system instructions from user input. Instruction shielding (clear separation of instruction and input) and input validation/sanitization are effective defenses. Adding more examples or adjusting temperature do not address injection.

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Increase temperature to make model less predictable

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Use a smaller model to reduce capability

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Add more few-shot examples with safe outputs

Question 12

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

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 13

What is the primary purpose of setting the 'top-k' parameter in an LLM?

Accepted Answer

To limit the next token selection to the k most probable tokens. Top-k restricts the model to sample only from the k most likely next tokens, reducing the chance of unusual or off-topic words. Top-p is nucleus sampling; temperature controls randomness; frequency penalty reduces repetition.

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To truncate the output after k tokens

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To control the creativity of the output by scaling logits

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To penalize tokens that have already appeared in the generated text

Question 14

A developer is using a Cohere Command model via OCI Generative AI. They want the model to generate responses strictly in JSON format for a specific task, but the model sometimes outputs additional explanatory text. Which prompt engineering technique is MOST effective?

Accepted Answer

Add a preamble: 'You are a JSON generator. Output only valid JSON. Do not include any other text.'. Using a system prompt to set the persona (e.g., 'You are a JSON generator') and including a step-by-step instruction reduces unwanted text. Cohere's preamble works like a system prompt to enforce constraints.

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Include a single example of a JSON output in the user message

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Set the temperature to 0.0 to make the model deterministic

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Use a stop sequence '}' to force the model to stop after the JSON

Question 15

A data scientist wants to generate a concise summary of a long legal document. The model should output a bullet list of key points. Which prompt component is LEAST important for this task?

Accepted Answer

Few-shot examples of summaries. The summary task does not require example inputs; zero-shot or few-shot can work, but the most critical components are the task instruction and output format. Examples are optional and least important.

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Context/background (the legal document text)

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Output format specification ('Output as a bullet list')

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Task instruction ('Summarize the following legal document in bullet points')