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NEW QUESTION # 20
For each of the following statement1, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to returning transcribed text. It is designed for real-time spoken conversational experiences and can include speech input, model reasoning, and spoken output.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services.
= No
Voice Live combines the conversational voice pipeline, so you do not need to separately build independent speech-to-text and text-to-speech services for the same real-time voice experience.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This statement is correct. Voice Live is used for real-time conversational AI experiences where spoken input is processed, the model reasons over the request, and spoken output can be returned.
NEW QUESTION # 21
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is endpoint of the model . After a vision-enabled GPT model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint that applications can call programmatically. The application sends requests containing text and, when required, image content to the deployed model through the appropriate chat-completions API endpoint. Microsoft's documentation explicitly states that to use vision- enabled models, you call the Chat Completion API against a supported model that has been deployed.
The Foundry playground is primarily an interactive environment for testing prompts, model behavior, and multimodal inputs during development. It is not the production interface that an external application uses to submit inference requests. An evaluation pipeline is used to assess model or application quality against defined metrics and datasets, while a training dataset is associated with model training or fine-tuning rather than serving inference traffic.
Microsoft Foundry provides the endpoint and required authentication information after deployment.
Applications then invoke that endpoint using an SDK or REST API, passing text and image inputs as part of the request payload.
NEW QUESTION # 22
You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?
Answer: C
NEW QUESTION # 23
You plan to create an Al 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: C
Explanation:
Use a provisioned deployment when the solution requires a specific allocated amount of throughput.
Microsoft states that provisioned throughput is a deployment type that lets you specify the amount of throughput required for a model deployment. Unlike standard deployments, where capacity is shared and throughput can vary, a provisioned deployment holds a fixed amount of processing capacity for the deployment.
Why the other options are incorrect:
A). global batch is for asynchronous batch processing, not dedicated real-time throughput allocation.
B). data zone standard and D. standard use shared capacity; they do not allocate a fixed, specified amount of throughput to the deployment.
NEW QUESTION # 24
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script.
You need to generate a narration audio file for each video based on the script.
Which type of workload should you use?
Answer: B
NEW QUESTION # 25
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