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

Question 1

A developer creates a prompt for a code generation task but the output often contains syntax errors. Which adjustment to the prompt is MOST likely to improve correctness?

Accepted Answer

Add a few examples of correct code for similar tasks in the prompt. Adding few-shot examples of correct code directly in the prompt provides the model with explicit patterns to follow, reducing syntactic drift. This technique, known as few-shot prompting, anchors the model's output to the demonstrated structure and syntax, which is far more effective than relying on the model's internal knowledge alone for code generation tasks.

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Remove the output format specification to give the model freedom

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Increase the temperature to 0.9 for more creative solutions

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Set the max tokens to a very high value

Question 2

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 (CoT) prompting explicitly instructs the model to generate intermediate reasoning steps before producing a final answer. This technique improves performance on tasks requiring multi-step logic, arithmetic, or commonsense reasoning by making the reasoning process explicit. The key is that the model is asked to 'think step by step' or similar, which elicits a sequence of thoughts leading to the answer.

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Tree-of-thought prompting

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Self-consistency

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Zero-shot prompting

Question 3

A prompt engineer wants the model to adopt a strict, professional tone for a financial report generation task. Which prompt component should be used to set this tone and persona?

Accepted Answer

System prompt. The system prompt is the correct component because it is specifically designed to set the model's behavior, tone, and persona at the beginning of a conversation. In the context of a financial report generation task, a system prompt like 'You are a professional financial analyst. Use a strict, formal tone.' instructs the model to adopt that persona for all subsequent interactions, ensuring consistency across the entire session.

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Few-shot examples

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User message

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Stop sequences

Question 4

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 penalizes tokens that have already appeared in the generated text, directly discouraging the model from repeating the same phrases. Increasing it reduces verbatim repetition by lowering the probability of previously used tokens. This is the most targeted parameter for the described symptom of repeated identical output.

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Decrease max tokens

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Increase temperature

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Increase top-p

Question 5

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.'. The core issue is that the model is drifting into other Romance languages, which is a language-consistency problem best solved by an explicit instruction constraint. Adding a system-prompt directive such as 'Translate only to French. Do not use any other language.' directly narrows the model's output distribution toward French tokens and is the most reliable, low-cost refinement. System prompts are weighted more heavily than user prompts in most LLM APIs, so this constraint is respected across turns.

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Use a stop sequence to halt after the first sentence

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Lower top-k to 1 for deterministic output

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Increase temperature to encourage more focused output

Question 6

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. Option A is correct because defining the JSON schema in the system prompt gives the model an explicit structural contract (field names, types, nesting, and required keys) that it must follow when generating the legal analysis output. Option B is correct because providing a concrete example of the desired JSON output (few-shot demonstration) anchors the model to the exact format, key naming, and value conventions expected, which is one of the most reliable ways to enforce structured output. Option C is not appropriate because a stop sequence of "}" would halt generation at the first closing brace, truncating any nested or multi-object JSON and producing invalid output. Option D is not essential because max tokens only caps response length; it does not enforce structure, and 5000 is an arbitrary value unrelated to schema compliance. Option E is not appropriate because temperature 1.0 increases randomness and would undermine the deterministic, strictly formatted JSON output required for legal document analysis.

Answer

Use a stop sequence of "}"

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Increase max tokens to 5000

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Set temperature to 1.0 for creativity

Question 7

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. Option A is correct because testing the prompt against a diverse set of input documents exposes it to varied lengths, domains, and edge cases, revealing weaknesses that a narrow sample would hide and enabling systematic generalization. Option B is correct because A/B testing prompt variants on a held-out set provides an unbiased, quantitative comparison of candidate prompts, so improvements are measured rather than assumed. Option E is correct because establishing clear evaluation criteria such as accuracy and conciseness gives an objective, repeatable rubric against which each iteration can be scored, which is the foundation of systematic refinement. Option C is not appropriate because reusing the same input guarantees consistency but prevents discovering failures on other inputs, defeating iterative improvement. Option D is not appropriate because raising max tokens to the maximum merely allows longer outputs; it does not improve summarization quality and can increase cost, latency, and verbosity without any evaluation-driven benefit.

Answer

Always use the same input to ensure consistency

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Increase max tokens to the maximum allowed

Question 8

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. The output format specification is the prompt component explicitly responsible for defining the structure, schema, or syntax of the model's response — such as 'return a JSON object with keys name, age, and email.' When a developer needs a specific machine-readable structure, this is the correct component to use. It is distinct from the task instruction (what to do) and constraints (limits on behavior).

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Task instruction

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Constraints

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Context/background

Question 9

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 prompting improves factual accuracy by generating multiple diverse reasoning paths for the same prompt and then selecting the most consistent answer through majority voting. This reduces hallucinations by leveraging the idea that correct reasoning tends to converge, while incorrect reasoning diverges. It is especially effective for tasks requiring complex reasoning, such as legal document summarization.

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Few-shot prompting

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Zero-shot prompting

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Chain-of-thought prompting

Question 10

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.

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top-k

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frequency_penalty

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top-p

Question 11

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?

Accepted Answer

Use a system prompt that includes both the desired tone and the constraint about medical advice. A system prompt is the correct place to encode persistent behavioral rules — desired tone and hard constraints — because it is applied to every turn and takes precedence in steering model behavior. Including both the polite/professional tone and the 'never provide medical advice' constraint in the system prompt ensures the rules apply consistently across all user interactions. This is the standard prompt-engineering pattern for durable guardrails.

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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 12

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 prompting specifically addresses flawed reasoning by sampling multiple diverse reasoning paths (typically with higher temperature) and aggregating the final answers, usually by majority vote. Because correct reasoning paths tend to converge on the same answer while flawed ones diverge, the aggregated answer is more reliable than any single chain.

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Role prompting

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Tree-of-thought prompting

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Few-shot prompting

Question 13

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. The template pattern is designed for generating structured output that follows a specific template every time. It provides a predefined format with placeholders that the model fills in, ensuring consistency. This is ideal for reports that must adhere to a standard structure.

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Recipe pattern

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ReAct pattern

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Role prompting

Question 14

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. Systematic prompt refinement requires an objective, repeatable measurement loop. Running A/B tests on a representative evaluation set and measuring accuracy gives a quantitative signal about which prompt version performs better, controlling for randomness and bias. This is the standard methodology for prompt optimization because it isolates the variable (the prompt) and measures the outcome against ground truth.

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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 15

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 is the technique of including a small number of input-output examples directly in the prompt so the model can infer the desired pattern, format, or reasoning style. By showing the model concrete demonstrations, it generalizes the task without any fine-tuning. This is distinct from zero-shot (no examples) and from chain-of-thought (which elicits step-by-step reasoning rather than demonstrating input-output pairs).

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Zero-shot prompting

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Self-consistency prompting

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Chain-of-thought prompting