This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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 developer is using OCI GenAI to generate structured data. They often get responses that include additional commentary or markdown. Which prompt engineering technique should they use to ensure only JSON output?
Set top_p to 0.1.
Why wrong: Incorrect: Low top_p reduces diversity but does not force JSON.
Use a model with a larger context window.
Why wrong: Incorrect: Larger context does not enforce output format.
Add 'Return only JSON' at the end of the prompt.
Correct: Direct instruction enforces format.
Increase the temperature to 1.5.
Why wrong: Incorrect: Higher temperature increases randomness, not format compliance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Add 'Return only JSON' at the end of the prompt.
Option C is correct because explicitly instructing the model to 'Return only JSON' directly constrains the output format, reducing the likelihood of extraneous commentary or markdown. This technique leverages prompt engineering to guide the model's behavior without altering inference parameters like temperature or top_p, which control randomness rather than output structure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Set top_p to 0.1.
Why it's wrong here
Incorrect: Low top_p reduces diversity but does not force JSON.
Use a model with a larger context window.
Why it's wrong here
Incorrect: Larger context does not enforce output format.
Add 'Return only JSON' at the end of the prompt.
Why this is correct
Correct: Direct instruction enforces format.
Related concept
Read the scenario before looking for a memorised answer.
Increase the temperature to 1.5.
Why it's wrong here
Incorrect: Higher temperature increases randomness, not format compliance.
Common exam traps
Oracle often tests the misconception that adjusting sampling parameters (like temperature or top_p) can enforce output format, when in fact these parameters control randomness and diversity, not structural constraints—leading candidates to overlook the direct prompt engineering solution.
Trap categories for this question
Command / output trap
Incorrect: Larger context does not enforce output format.
Detailed technical explanation
Prompt engineering techniques like explicit format instructions (e.g., 'Return only JSON') work by conditioning the model's autoregressive generation on a clear constraint, often reinforced by few-shot examples. Under the hood, this leverages the model's instruction-following capability, which is distinct from sampling parameters like temperature or top_p that affect the probability distribution of token choices. In real-world scenarios, combining explicit instructions with system messages or role-based prompts (e.g., 'You are a JSON-only assistant') further improves reliability, especially when generating structured data for API integrations.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
The correct answer is: Add 'Return only JSON' at the end of the prompt. — Option C is correct because explicitly instructing the model to 'Return only JSON' directly constrains the output format, reducing the likelihood of extraneous commentary or markdown. This technique leverages prompt engineering to guide the model's behavior without altering inference parameters like temperature or top_p, which control randomness rather than output structure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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