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

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Total 50 questions
Question 1

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 2

You have a Microsoft Foundry project that contains a generative AI model deployment.

You test the model by using the Foundry playground.

You need to develop an application that sends requests to the deployed model.

Which information must the application include to call the model?



Answer : D

To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.

The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.


Question 3

You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience.

Which AI workload should you use?



Answer : A

Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user's prompt.

B . computer vision is for interpreting images or video. C . text classification categorizes existing text, but does not draft new marketing emails. D . speech recognition converts spoken audio into text.

Therefore, the correct answer is A. generative AI.


Question 4

You have an Azure subscription.

You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.

What should you provision?



Answer : B

To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.

The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.

Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.


Question 5

Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.

You need to generate text transcripts of all the recordings.

Which Azure Speech in Foundry Tools capability should you use?



Answer : A

For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription.

Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage, including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.

Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio. Speech translation translates speech between languages, but the requirement is to generate transcripts.


Question 6

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 : A

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


Question 7

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.


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Total 50 questions