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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Microsoft Azure AI Fundamentals Sample Questions (Q79-Q84):

NEW QUESTION # 79
You need to deploy a generative AI model to a Microsoft Foundry project. Which factor should you use to identify which type of model can be deployed to the project?

Answer: C

Explanation:
Yes, Azure region availability determines whether you can use a Global, Data Zone, or Regional/Standard deployment type for your generative AI model.
Deployment Types and Region Dependencies
Global Deployments (Global Standard / Global Provisioned): Use these when there are no strict single-region data residency constraints; traffic and inferencing may route to any Azure region where the model is actively supported and has capacity.
Data Zone Deployments (Data Zone Standard / Data Zone Provisioned): Use these when data processing must be restricted to a specific compliance boundary (such as the EU or US data zone), requiring your project to reside in a region belonging to that designated zone.
Regional Deployments (Standard / Regional Provisioned): Use these when your workload requires data processing to stay strictly within the single Azure region associated with your project.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/reference/region-support


NEW QUESTION # 80
You deploy a model named gpt-image-1 in Microsoft Foundry.
You need to ensure that an application can generate an image from a user prompt by using the OpenAI Images API.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
images.generate
The canonical Python call is:
response = client.images.generate(
model= " gpt-image-1 " ,
prompt=prompt
)
The correct selection is images.generate . Microsoft's current Foundry documentation shows that image generation with a deployed GPT Image model uses the OpenAI Python SDK ' s Images API , specifically client.images.generate(). Microsoft provides an example using model= " gpt-image-1 " together with a text prompt, which directly corresponds to this scenario.
chat.completions.create is intended for chat-completion interactions rather than the standalone Images API.
images.create is not the documented Python SDK method for generating an image. responses.create belongs to the Responses API , which can also orchestrate image generation through an image-generation tool, but that is a different API pattern from the explicitly requested OpenAI Images API . Microsoft distinguishes the standalone image-generation API from image generation performed through the Responses API.
One important detail in the supplied exhibit is that it visually shows client.responses. before the dropdown.
Taken literally, client.responses.images.generate() would not be the documented syntax. For this question, the intended answer is clearly images.generate , yielding the documented form client.images.generate(...) .
Study Guide Reference: Microsoft Azure AI Fundamentals # Implement AI solutions by using Microsoft Foundry # Generative AI and image-generation models.


NEW QUESTION # 81
You are building a knowledge base by using Azure AI Language service's custom answering feature. Which file format can you use to populate the knowledge base?

Answer: C

Explanation:
Azure AI Language service's custom answering can populate its knowledge base using various file formats, including structured text (.txt, .tsv), Microsoft Office documents (.docx, .xlsx, .pptx), and HTML, PDF, and Markdown files.
Reference:
https://docs.azure.cn/en-us/ai-services/language-service/question-
answering/reference/document-format-guidelines


NEW QUESTION # 82
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?

Answer: B

Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft's quickstart also shows invoice processing with the prebuilt- invoice analyzer to extract structured data from an invoice document.


NEW QUESTION # 83
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?

Answer: C

Explanation:
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.


NEW QUESTION # 84
......

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