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Microsoft Certified professionals are often more sought after than their non-certified counterparts and are more likely to earn higher salaries and promotions. Moreover, cracking the Microsoft Azure AI Fundamentals (AI-901) exam helps to ensure that you stay up to date with the latest trends and developments in the industry, making you more valuable assets to your organization.

Microsoft AI-901 Exam Syllabus Topics:

Topic	Details
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 (Q28-Q33):

NEW QUESTION # 28
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?

A. Call begin_analyze(), and then call poller.result() to retrieve the results.
B. Use optical character recognition (OCR) to extract text from the PDF and map the fields in the code.
C. Call analyze() to return the extracted fields synchronously in the same request.
D. Submit the PDF to an analyzer and read the results from the request headers.

Answer: A

Explanation:
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.

NEW QUESTION # 29
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct answer is Creating captions for a video recording. Speech recognition, also called speech-to-text, converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice- activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis.

NEW QUESTION # 30
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. output_image
D. image_generation

Answer: B

Explanation:
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.

NEW QUESTION # 31
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?

A. none
B. required
C. auto

Answer: B

Explanation:
Yes, you should set tool_choice="required" if you want to force the model to pick a tool.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-best-practice

NEW QUESTION # 32
Select the answer that correctly complete the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
Information extraction solutions that detect and read text in scanned documents and images rely on computer vision .
Detecting and reading text in scanned documents and images is typically done with OCR , which is a computer vision capability. Microsoft describes OCR as text recognition/text extraction that extracts printed or handwritten text from images and documents.

NEW QUESTION # 33
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