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What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
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A, B
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Explanation
Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave. Option A is correct because instructions can define constraints the model must follow. Option B is correct because instructions can define the agent’s role and behavior. Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions. |
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
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Answer:
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Explanation
client = OpenAI( api_key="...", base_url="https://resource1.openai.azure.com/openai/v1/", ) response = client.responses.create( model="my-mini-gpt", ) For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format: https:// < resource-name > .openai.azure.com/openai/v1/ In the question, the Azure OpenAI resource is named Resource1 , so the first blank must be resource1 . Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.azure.com/openai/v1/ path. For the model parameter, Azure OpenAI requires the deployment name , not the underlying model name. Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model. The deployed model is gpt-4.1-mini , but the deployment name is my-mini-gpt . Therefore, the second blank must be: model="my-mini-gpt" So the correct selections are: base_url blank = resource1 model blank = my-mini-gpt |
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.
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Answer:
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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. |
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Find answers to the most common questions about the Microsoft AI-901 exam, including what it is, how to prepare, and how it can boost your career.
The Microsoft AI-901 certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the Microsoft AI-901 certification syllabus. The Microsoft AI-901 certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in Microsoft AI-901 certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, Microsoft AI-901 certification may require periodic renewal to stay current with new innovations in the concerned domains.
