1Z0-1127-25 Prompt Engineering Practice Test 4 — 15 Questions

Question 1

Which prompting technique involves generating multiple independent reasoning paths and then selecting the most common answer?

Accepted Answer

Self-consistency prompting. Self-consistency runs chain-of-thought multiple times and aggregates answers (e.g., by majority vote) to improve reliability. The other options are different techniques.

Answer

Chain-of-thought prompting

Answer

Few-shot prompting

Answer

Zero-shot prompting

Question 2

An OCI Generative AI user notices that a model generates repetitive phrases when summarizing technical articles. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty. Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly targeting repetition. Presence penalty also helps but frequency penalty is stronger for repeated phrases.

Answer

Decrease max tokens

Answer

Set top-p to 0.95

Answer

Increase temperature to 0.9

Question 3

What is the main advantage of using chain-of-thought (CoT) prompting over standard few-shot prompting for complex reasoning tasks?

Accepted Answer

CoT improves performance on multi-step reasoning tasks by generating step-by-step explanations. CoT encourages the model to articulate intermediate reasoning steps, which improves accuracy on tasks that require multi-step logic. Few-shot provides examples but not the reasoning process.

Answer

CoT reduces the number of tokens needed in the prompt

Answer

CoT allows the model to use any tools available in the environment

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CoT eliminates the need for any examples in the prompt

Question 4

A prompt engineer is testing two versions of a prompt for a content generation task. They want to measure which version produces more factual and concise outputs. Which evaluation approach is BEST?

Accepted Answer

Run A/B tests on a diverse set of inputs and score outputs based on predefined criteria. A/B testing with clear metrics (factuality, conciseness) is the standard method for comparing prompt variants. Manual inspection on a few cases is not statistically robust; other options are not comparative.

Answer

Use only the first output from each prompt and manually compare

Answer

Ask the model to self-evaluate its outputs

Answer

Increase the temperature to see which prompt handles randomness better

Question 5

Which of the following is a common prompt injection vulnerability?

Accepted Answer

User input that contains 'Ignore previous instructions' followed by malicious commands. Prompt injection occurs when user input overrides the system's intended instructions. An attacker can inject 'Ignore previous instructions' to bypass safety guardrails.

Answer

Including too many few-shot examples

Answer

Setting temperature too high

Answer

Using a system prompt that is too long

Question 6

A developer uses a Cohere Command model with a preamble: 'You are a helpful assistant that answers questions based on the provided document.' They also include a user message with a query and a relevant document excerpt. However, the model often ignores the document and uses its own knowledge. Which improvement is MOST effective?

Accepted Answer

Use the document-grounded generation format with and tags around the document. Cohere's document-grounded generation syntax uses special tags to clearly separate the document from the query, ensuring the model focuses on the provided content rather than its training data.

Answer

Increase the temperature to 0.7 to reduce reliance on parametric knowledge

Answer

Set frequency penalty to 0.5

Answer

Remove the preamble and rely solely on the user message

Question 7

A team wants to use a prompt pattern that combines reasoning with acting, where the model alternates between thinking and taking actions (e.g., tool calls). Which pattern fits this requirement?

Accepted Answer

ReAct pattern. The ReAct pattern (Reason + Act) prompts the model to output reasoning steps and then perform actions (like API calls), enabling interactive problem-solving.

Answer

Template pattern

Answer

Role prompting

Answer

Recipe pattern

Question 8

A prompt engineer is designing a system to answer customer queries using OCI Generative AI. They want to ensure the model's responses are concise, accurate, and consistent. Which TWO prompt engineering practices should they implement? (Select two.)

Accepted Answer

Include a system prompt specifying 'You are a customer support agent. Answer concisely in two sentences.'. Setting a low temperature reduces randomness (consistency). Including a system prompt with tone and length constraints directly addresses conciseness and format.

Answer

Remove any context from the prompt to avoid biasing the model

Answer

Use the tree-of-thought technique for every query

Answer

Set temperature to 0.9 to encourage creative responses

Question 9

An OCI user is troubleshooting a prompt that sometimes produces outputs containing offensive language. The prompt uses a system prompt to set a professional tone. Which THREE steps should the user take to mitigate this issue? (Select three.)

Accepted Answer

Apply a frequency penalty to discourage repetition of offensive phrases. Adding explicit constraints in the system prompt, setting frequency/presence penalties to reduce undesirable patterns, and testing with adversarial inputs are effective safeguards. Using high temperature or removing the system prompt would worsen the problem.

Answer

Increase temperature to 0.9 to dilute the offending outputs

Answer

Remove the system prompt to avoid overriding model's safety training

Question 10

A company uses OCI Generative AI to generate product descriptions in XML format. The engineer wants to improve adherence to the XML schema. Which THREE prompt components are most critical? (Select three.)

Accepted Answer

Output format specification: 'Use the following XML schema: ...'. Task instruction tells the model what to do, output format specification tells it the structure, and few-shot examples provide a concrete reference. Context/background is less critical, and temperature does not enforce schema.

Answer

Setting temperature to 0.9

Answer

Context/background about the company's product line

Question 11

A data scientist wants to generate a response that lists top-3 product features in a bulleted format with no introductory text. Which prompt engineering technique should be used?

Accepted Answer

Include a clear task instruction specifying the format. Specifying the output format (bulleted list, no intro text) directly in the instruction is the most straightforward way to control the structure. Few-shot examples could also work but are unnecessary when a clear instruction suffices.

Answer

Set temperature to 0 to ensure deterministic output

Answer

Provide few-shot examples of desired output

Answer

Use chain-of-thought prompting

Question 12

What is the primary benefit of using a system prompt to set the persona and tone before the user message?

Accepted Answer

It sets the overall behavior, tone, and constraints for the model throughout the conversation. The system prompt establishes persistent behavioral guidelines that influence all subsequent interactions, ensuring consistency without repeating instructions in every user message.

Answer

It reduces the token cost of each user message

Answer

It automatically grounds the model in the latest training data

Answer

It replaces the need for few-shot examples

Question 13

A developer is using Cohere Command to answer questions grounded in internal technical manuals. They want to ensure the model only answers based on the provided documents and does not use its pre-trained knowledge. Which Cohere-specific technique should be applied?

Accepted Answer

Use the document-grounded generation syntax by providing documents in the chat history with explicit citation instructions. Cohere's document-grounded generation syntax allows you to supply search results or documents and instruct the model to answer solely from those documents, reducing hallucination.

Answer

Fine-tune the model on the technical manuals

Answer

Set temperature to 0 in the generation parameters

Answer

Use the preamble to instruct the model to answer only from the documents

Question 14

An AI engineer is designing a prompt that requires the model to solve a complex math problem. They want the model to explore multiple reasoning paths and then aggregate the final answer. Which prompting technique BEST fits this requirement?

Accepted Answer

Self-consistency prompting. Self-consistency generates multiple reasoning paths (e.g., using chain-of-thought) and then takes a majority vote or averages results, which directly matches the need for multiple paths and aggregation.

Answer

Zero-shot prompting with a direct instruction

Answer

Tree-of-thought prompting

Answer

Few-shot prompting with worked examples

Question 15

A prompt engineer notices that the model's output frequently repeats the same phrases. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty. Frequency penalty reduces the chance of repeating tokens that have already appeared, directly mitigating repetition. Presence penalty also helps but is broader; frequency penalty is more targeted.

Answer

Increase max tokens

Answer

Decrease top-p to 0.5

Answer

Increase temperature to 0.9