Microsoft Azure AI Fundamentals (Updated Version) AI-901 Exam Questions

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Total 50 questions
Question 1

You need to convert written customer notifications into natural-sounding spoken audio that can be played over a phone system.

Which Azure Speech in Foundry Tools capability should you use?



Answer : B

The requirement is to convert written customer notifications into natural-sounding spoken audio. This is speech synthesis, also known as text to speech.

Microsoft's Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis.

Why the other options are incorrect:

A . speaker recognition identifies or verifies speakers by voice. C . speech recognition converts spoken audio into text. D . speech translation translates spoken audio between languages.


Question 2

You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.

You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.

What should you do?



Answer : A

Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().

Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:

result = poller.result()

Option B is incorrect because extraction results are not read from request headers. Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline. Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.


Question 3

You are developing an AI-powered customer support application.

Which task is an example of the Microsoft responsible AI principle of inclusiveness?



Answer : B

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.

Why the other options are incorrect:

A . Provide explanations about how predictions are generated = Transparency C . Evaluate model outputs across demographic groups to reduce bias = Fairness D . Encrypt stored customer data and restrict access by using role-based controls = Privacy and security


Question 4

You have a Microsoft Foundry project that contains an agent named Agent1.

You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.

To what should you set tool_choice for Agent1?



Answer : C

Microsoft's Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:

auto means the model decides whether to call tools. required means the model must call one or more tools. none means the model does not call tools.

Therefore:

A . auto = Incorrect, because the model may or may not call the Azure function. B . none = Incorrect, because this prevents tool/function calls. C . required = Correct, because it forces the agent to call a tool.

The Azure OpenAI function-calling documentation also confirms that tool_choice='auto' lets the model decide whether to call a function, while tool_choice='none' forces a user-facing response without a tool call.


Question 5

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 : C

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.

Why the other options are incorrect:

A . transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction. B . OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema. D . Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.

Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools.


Question 6

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.



Answer : A, B

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.


Question 7

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?



Answer : D

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.


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