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NEW QUESTION # 36
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?
Answer: A
Explanation:
1. Authentication Data
You must prove your application has permission to access the model. This is included inside the HTTP Headers:
API Key Authentication: Pass the secret key generated by your Foundry project inside the api-key header.
api-key: YOUR_FOUNDRY_SECRET_KEY
Microsoft Entra ID Authentication (Recommended for production): Instead of a static key, pass a dynamic bearer token inside the Authorization header.
Authorization: Bearer YOUR_ENTRA_ACCESS_TOKEN
2. Connection & Routing Data
Your application needs to know exactly where to send the web request. This data is provided via the HTTP Request URL (Endpoint):
Target URI / Base Endpoint: The primary destination host. For standard deployments, it typically aligns with the OpenAI/v1 compatible schema or the generic Azure AI Inference API format.
Deployment Name: Included in either the URL path
(/deployments/{deployment_name}/chat/completions) or inside the request payload body under the "model" parameter to ensure the request is routed to your specific instance.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/deploy-models-managed
NEW QUESTION # 37
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use , which maps directly to the Microsoft responsible AI principle of reliability and safety .
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety .
NEW QUESTION # 38
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 39
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?
Answer: C
Explanation:
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice.
This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 40
For each of the following statements, select Yes if the statement is true, Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes. Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes. A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No. Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.
NEW QUESTION # 41
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