- A
Set the temperature parameter to a low value (e.g., 0).
Why wrong: Temperature controls randomness; a low value makes output more deterministic but does not guarantee valid JSON format.
- B
Set the top_p parameter to a high value (e.g., 1).
Why wrong: Top_p controls nucleus sampling; a high value allows more vocabulary but does not enforce a JSON structure.
- C
Set the response_format parameter to 'json_object'.
Setting response_format to 'json_object' instructs the model to output valid JSON, which is exactly what the developer needs for structured data generation.
- D
Set the max_tokens parameter to a high value (e.g., 2000).
Why wrong: Max_tokens limits the response length but does not control the format or structure of the output.
Quick Answer
The correct answer is to set the `response_format` parameter to `json_object`. This Azure OpenAI feature enforces valid JSON output at the API level, meaning the model will always return structured data that conforms to JSON syntax without requiring the developer to add formatting instructions in every prompt. By configuring this parameter, the service automatically constrains the model’s response to a valid JSON object, eliminating the risk of malformed output and saving development time. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to reliably generate structured data from Azure OpenAI, often appearing in scenario-based questions about API configuration. A common trap is confusing this with prompt engineering or system messages, but the key distinction is that `response_format` is a programmatic setting, not a textual instruction. Memory tip: think “JSON Object” as “Just Set One Parameter” — it’s a single API switch that guarantees valid JSON every time.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 Azure OpenAI Service to generate structured data in JSON format. They want to ensure that every response is valid JSON without adding instructions in every prompt. Which Azure OpenAI feature should they configure?
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
Set the response_format parameter to 'json_object'.
Option C is correct because Azure OpenAI Service provides a `response_format` parameter that can be set to `json_object`, which instructs the model to always return valid JSON output. This ensures structured data without requiring the developer to include formatting instructions in every prompt, as the service enforces JSON schema compliance at the API level.
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.
- ✗
Set the temperature parameter to a low value (e.g., 0).
Why it's wrong here
Temperature controls randomness; a low value makes output more deterministic but does not guarantee valid JSON format.
- ✗
Set the top_p parameter to a high value (e.g., 1).
Why it's wrong here
Top_p controls nucleus sampling; a high value allows more vocabulary but does not enforce a JSON structure.
- ✓
Set the response_format parameter to 'json_object'.
Why this is correct
Setting response_format to 'json_object' instructs the model to output valid JSON, which is exactly what the developer needs for structured data generation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the max_tokens parameter to a high value (e.g., 2000).
Why it's wrong here
Max_tokens limits the response length but does not control the format or structure of the output.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse parameters that control randomness (temperature, top_p) or output length (max_tokens) with those that enforce output structure, leading them to incorrectly assume that low temperature alone can produce consistent JSON formatting.
Trap categories for this question
Command / output trap
Temperature controls randomness; a low value makes output more deterministic but does not guarantee valid JSON format.
Detailed technical explanation
How to think about this question
The `response_format` parameter in Azure OpenAI Service leverages a constrained decoding mechanism that forces the model's output to adhere to a JSON schema, effectively filtering out non-JSON tokens during generation. This is implemented via a grammar-based sampling approach, which differs from post-processing validation. In real-world scenarios, this is critical for applications like automated data pipelines or API integrations where malformed JSON would cause parsing errors and require retries.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the response_format parameter to 'json_object'. — Option C is correct because Azure OpenAI Service provides a `response_format` parameter that can be set to `json_object`, which instructs the model to always return valid JSON output. This ensures structured data without requiring the developer to include formatting instructions in every prompt, as the service enforces JSON schema compliance at the API level.
What should I do if I get this AI-900 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: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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