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NEW QUESTION # 130
Drag and Drop Question
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right.
Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Transcribes spoken input
Speech recognition in Microsoft Foundry transcribes spoken input into text using built-in speech- to-text capabilities.
Transcription FeaturesReal-time transcription: Converts microphone inputs or live audio streams into text instantly.
Batch transcription: Processes pre-recorded audio or video files asynchronously.
Advanced audio models: Leverages tools like GPT-transcribe and GPT-live-transcribe to handle natural speech patterns, numbers, addresses, and industry-specific terminology.
Broad language support: Handles over 140 locales and languages for spoken input.
Box 2: Generates spoken output
Speech synthesis in Microsoft Foundry (via Azure Speech in Foundry Tools) converts text input into realistic, human-like spoken audio output.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text
NEW QUESTION # 131
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
Answer: B
Explanation:
A DALL-E REST API example using parameters like prompt, size, style, and n (number of images) can be used to generate images. Here's how:
Example Request Body (JSON):
{
"prompt": "A cat wearing a hat, standing on a skateboard",
"size": "1024x1024", // Supported values: "1024x1024", "1792x1024", "1024x1792"
"style": "natural", // DALL-E 3 supports "natural" and "vivid" styles
"n": 1 // DALL-E 3 only supports n=1
}
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/dall-e
NEW QUESTION # 132
You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.
You need to perform speech-to-text transcription from the microphone.
Which Python class should you use?
Answer: D
NEW QUESTION # 133
In the Microsoft Foundry portal, you create an agent named Agent1, and then deploy the agent.
You open the Foundry playground to test Agent1.
What does the playground use to ensure that the tests reflect production behavior?
Answer: B
Explanation:
The Configuration of the Agent: When you open the playground for a deployed agent in the Microsoft Foundry portal, it connects directly to the active deployment and uses the actual configuration, version, model, and tool bindings of the deployed agent to mirror real production logic.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5564123/how-to-enable-the-agents-playground-feature-in-azu
NEW QUESTION # 134
You plan to create an AI application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?
Answer: D
Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md
NEW QUESTION # 135
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