AI-901指南 & AI-901考試題庫
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AI-901考試題庫 & AI-901資料
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Microsoft AI-901 考試大綱:
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最新的 Microsoft Certified: Azure AI Fundamentals AI-901 免費考試真題 (Q63-Q68):
問題 #63
You are developing an application that processes voicemail recordings by using Azure Content Understanding in Foundry Tools.
Which feature does Azure Content Understanding use to convert audio to text?
- A. key phrase extraction
- B. Voice Live
- C. optical character recognition (OCR)
- D. transcription
答案:D
解題說明:
Azure Content Understanding uses transcription to convert audio content, such as voicemail recordings, into text. Microsoft's Azure Content Understanding audio documentation states that transcription converts conversational audio into searchable and analyzable text-based transcripts.
Option A. Voice Live is not the feature used by Content Understanding to convert voicemail recordings into text.
Option B. key phrase extraction identifies important phrases after text is available; it is not the audio-to-text conversion step.
Option D. optical character recognition (OCR) is for extracting text from images or documents, not audio.
Therefore, the correct answer is C. transcription.
問題 #64
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?
- A. Microsoft Visual Studio Code
- B. Copilot for Microsoft 365
- C. Azure AI Studio
- D. GitHub Actions
答案:C
解題說明:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry
問題 #65
You deploy the Azure OpenAI service to generate images.
You need to ensure that the service provides the highest level of protection against harmful content.
What should you do?
- A. Configure the system prompt
- B. Configure the Content filters settings
- C. Customize a large language model (LLM)
- D. Change the model used by the Azure OpenAI service
答案:B
解題說明:
Azure OpenAI, Content filtering
Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system works by running both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter
問題 #66
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
答案:
解題說明:
問題 #67
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?
- A. image_base64
- B. input_image
- C. image_generation
- D. output_image
答案:B
解題說明:
When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:
{"type": "input_image", "image_url": image_url}
Microsoft's Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.
Why the other options are incorrect:
A . image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image.
B . image_generation = Incorrect. This is related to generating images, not providing an image for analysis.
C . output_image = Incorrect. This would refer to generated output, not image input.
D . input_image = Correct.
問題 #68
......
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