Quick Answer
The answer is max_tokens, along with messages and temperature, as three valid parameters for the Azure OpenAI chat completions API. The messages parameter is required because it structures the conversation history as an array of objects with role and content fields, providing the context the model needs to generate a coherent response. Without messages, the API has no prompt to work from, making it the foundational parameter. On the AI-102 exam, this question tests your understanding of the API’s request body structure, often appearing as a multiple-select item where distractors like top_p or frequency_penalty are valid but not among the three listed. A common trap is confusing required parameters with optional ones—remember that messages is mandatory, while max_tokens controls response length and temperature influences creativity. For a memory tip, think of the three Ms: Messages, Max_tokens, and Model (the deployment name), as these are the core trio you’ll always specify in a chat completion call.
This AI-102 practice question tests your understanding of implement generative ai solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Which THREE are valid parameters when calling the Azure OpenAI Service chat completions API?
messages
Required input.
index_name
Why wrong: Not a chat completions parameter.
temperature
Controls randomness.
max_tokens
Controls response length.
embedding_model
Why wrong: Not a chat completions parameter.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
messages
The `messages` parameter is required in the Azure OpenAI Service chat completions API call. It defines the conversation history and user input as an array of message objects, each with a `role` (system, user, assistant) and `content`. Without this parameter, the API cannot determine the context or prompt for generating a response.
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.
messages
Why this is correct
Required input.
Related concept
Read the scenario before looking for a memorised answer.
index_name
Why it's wrong here
Not a chat completions parameter.
temperature
Why this is correct
Controls randomness.
Related concept
Read the scenario before looking for a memorised answer.
max_tokens
Why this is correct
Controls response length.
Related concept
Read the scenario before looking for a memorised answer.
embedding_model
Why it's wrong here
Not a chat completions parameter.
Common exam traps
Microsoft often tests the distinction between parameters for different Azure OpenAI API endpoints (chat completions vs. embeddings vs. search), so candidates may confuse `index_name` or `embedding_model` as valid chat completions parameters due to their familiarity with other Azure AI services.
Detailed technical explanation
The `temperature` parameter controls randomness in token sampling (0.0 to 2.0, default 1.0), where lower values make output more deterministic. The `max_tokens` parameter limits the total tokens in the response, including both input and output tokens, and is capped by the model's context window (e.g., 4096 for GPT-3.5-Turbo). Under the hood, the API uses a transformer-based autoregressive decoder that samples from the probability distribution of the next token, adjusted by temperature and top_p.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: messages — The `messages` parameter is required in the Azure OpenAI Service chat completions API call. It defines the conversation history and user input as an array of message objects, each with a `role` (system, user, assistant) and `content`. Without this parameter, the API cannot determine the context or prompt for generating a response.
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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