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NEW QUESTION # 47
Based on the image provided, here is the transcribed text:
You need to build an AI solution that produces new product images based on written descriptions provided by users.
Which AI workload should you use?
Answer: B
Explanation:
The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.
Why the other options are incorrect:
B . image analysis is used to examine and interpret existing images.
C . object detection is used to identify and locate objects within an existing image.
D . optical character recognition (OCR) is used to extract text from images or scanned documents.
Since the solution must generate new visual content from user-provided descriptions, the correct answer is:
A . image generation
NEW QUESTION # 48
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video, and what to extract, such as text, layout, fields, transcripts, and structured JSON output.
Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt- image.
For scanned invoices in PDF format, the correct choice is document analyzer, because invoices are document
/form content. Microsoft's Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.
For voicemail recordings in WAV format, the correct choice is audio analyzer, because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.
NEW QUESTION # 49
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 # 50
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data.
Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.
The other options are incorrect:
a CSV file attachment is not an image input format for vision analysis.
an MP4 video stream is video content, not an image format.
a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.
NEW QUESTION # 51
What should you use to compare benchmark metrics of different AI models?
Answer: D
Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog
NEW QUESTION # 52
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