- A
Post-process the output with a regex to fix formatting
Why wrong: Post-processing can fix some issues but is brittle and may not handle all variations; prompt-level technique is preferred.
- B
Provide a few-shot example with the desired JSON format
Few-shot examples explicitly show the model the exact structure, leading to more consistent outputs than instructions alone.
- C
Use a system prompt that says 'output JSON'
Why wrong: A simple instruction may not be enough to guarantee consistent structure; the model might deviate.
- D
Use a larger model with better formatting capabilities
Why wrong: Larger models are not guaranteed to follow formatting instructions; examples are more reliable.
Question 245 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to deploy a GenAI system for contract analysis that must produce structured output (e.g., JSON) for downstream processing. They need to ensure the output format is consistent. Which prompt engineering technique is MOST effective?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Provide a few-shot example with the desired JSON format
Option B is correct because providing a few-shot example with the desired JSON format explicitly demonstrates the exact structure, keys, and nesting required, which guides the model to generate consistent structured output. This technique leverages in-context learning, where the model infers the output pattern from the examples, making it more reliable than vague instructions or post-processing fixes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Post-process the output with a regex to fix formatting
Why it's wrong here
Post-processing can fix some issues but is brittle and may not handle all variations; prompt-level technique is preferred.
- ✓
Provide a few-shot example with the desired JSON format
Why this is correct
Few-shot examples explicitly show the model the exact structure, leading to more consistent outputs than instructions alone.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a system prompt that says 'output JSON'
Why it's wrong here
A simple instruction may not be enough to guarantee consistent structure; the model might deviate.
- ✗
Use a larger model with better formatting capabilities
Why it's wrong here
Larger models are not guaranteed to follow formatting instructions; examples are more reliable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that a vague instruction like 'output JSON' or a larger model alone can guarantee format consistency, when in fact explicit few-shot examples are required to enforce a specific schema and reduce output variability.
Detailed technical explanation
How to think about this question
Few-shot prompting works by placing the model in a 'pattern completion' mode, where it treats the provided examples as part of the context and statistically predicts the most likely continuation. Under the hood, transformer models use attention mechanisms to weigh the similarity between the example and the new input, making the output format highly reproducible when examples are clear and diverse. In real-world contract analysis, a single missing comma or incorrect key name can break downstream JSON parsers, so few-shot examples with explicit field names (e.g., 'party_name', 'effective_date') and nested objects ensure robust integration with automated pipelines.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Provide a few-shot example with the desired JSON format — Option B is correct because providing a few-shot example with the desired JSON format explicitly demonstrates the exact structure, keys, and nesting required, which guides the model to generate consistent structured output. This technique leverages in-context learning, where the model infers the output pattern from the examples, making it more reliable than vague instructions or post-processing fixes.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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