20+ practice questions focused on Describe features of generative AI workloads on Azure — one of the most tested topics on the Microsoft Azure AI Fundamentals AI-900 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Describe features of generative AI workloads on Azure PracticeA developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
Explanation: Temperature controls randomness in Azure OpenAI. Lowering the temperature parameter makes the output more focused and deterministic. Max tokens controls length, while presence and frequency penalties influence repetition rather than core randomness.
A quality assurance team at a software company uses Azure OpenAI Service to generate compliance reports. They need the model to produce the exact same output for a given prompt every time the API is called, to ensure reproducibility during testing. Which parameter should they set to achieve this deterministic behavior?
Explanation: Setting temperature to 0 forces the model to choose the most likely token at each step, eliminating randomness and producing deterministic outputs for the same prompt. This is essential for reproducibility in testing scenarios where identical results are required across API calls.
A marketing team uses Azure OpenAI Service to generate taglines for a new advertising campaign. They want the output to be more predictable and less surprising, sticking to the most common phrases and avoiding unusual combinations. Which parameter should they decrease?
Explanation: Temperature controls randomness by scaling token probabilities; decreasing it favors high-probability tokens. Top P (nucleus sampling) controls the cumulative probability threshold; decreasing it also restricts outputs to a smaller set of probable tokens. Both make output more conservative and predictable. The original explanation is misleading because reducing Top P also avoids unusual combinations.
A marketing team wants to use a generative AI model to produce social media posts that match their brand's specific tone and style. They have a small set of example posts written by their copywriters. Which approach should they use to customize the model's outputs without retraining the entire model?
Explanation: Prompt engineering, especially few-shot prompting, allows the model to match a desired tone and style by providing example posts in the instruction. It does not involve any training or weight updates, satisfying the 'without retraining' requirement. Fine-tuning (B) requires a large dataset and involves further training, which conflicts with the constraint. Grounding (C) adds context but does not effectively customize style, and content filtering (D) is for safety, not style.
A developer is using Azure OpenAI to generate code snippets for a banking application. The developer wants to minimize the risk that the generated code contains security vulnerabilities or malicious instructions, even if the prompt is ambiguous. Which Azure OpenAI feature should the developer configure to address this concern?
Explanation: A system message guides the model to follow secure coding practices, directly addressing security vulnerabilities. Content filters are a safety layer for harmful content but are not designed to identify code-level vulnerabilities.
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