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Microsoft AI-901 Dumps - Pass Microsoft Azure AI Fundamentals (Updated Version) Exam in First Attempt 2026
The Microsoft AI-901 - Microsoft Azure AI Fundamentals (Updated Version) exam is part of the Microsoft Azure certification path. It is designed for candidates who want to build a strong foundation in artificial intelligence concepts and learn how AI solutions are implemented in the Microsoft ecosystem. This exam is a valuable starting point for learners, beginners, and professionals who want to validate essential AI knowledge. Earning this certification can help demonstrate your readiness for modern cloud and AI-focused roles.
| # | Exam Topics | Sub-Topics | Approximate Weightage (%) |
|---|---|---|---|
| 1 | Identify AI concepts and capabilities |
|
50% |
| 2 | Implement AI solutions by using Microsoft Foundry |
|
50% |
The exam tests your understanding of foundational AI knowledge and your ability to recognize how Microsoft tools support AI solution implementation. Candidates should expect a mix of concept-based questions and practical scenario questions that measure real understanding rather than simple memorization. Strong preparation should cover both AI fundamentals and the use of Microsoft Foundry in solution workflows.
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Frequently Asked Questions
Who should take the Microsoft AI-901 exam?
The exam is suitable for candidates who want to validate foundational knowledge of AI concepts and Microsoft Azure AI capabilities. It is a good fit for beginners and learners exploring AI fundamentals.
Is the Microsoft Azure AI Fundamentals exam difficult?
The difficulty depends on your preparation and familiarity with AI basics. Since it covers both concepts and Microsoft Foundry implementation, candidates should study the topics carefully and practice with exam-style questions.
Can I pass AI-901 with only braindumps?
Braindumps alone are not the best approach if your goal is reliable understanding. A better strategy is to use dumps together with practice tests and review the concepts so you can answer scenario-based questions with confidence.
Do I need hands-on experience with Microsoft Foundry?
Hands-on experience can help, but the exam is designed around foundational knowledge. If you understand the core concepts and practice the question format, you can prepare effectively even if you are still building practical experience.
Are QA4Exam.com dumps enough to pass the exam?
QA4Exam.com exam PDF and Online Practice Test are strong preparation tools, but the best results come from combining them with topic review. This helps you understand the material, verify answers, and improve exam readiness.
How do the QA4Exam.com practice tests help with first attempt success?
The practice tests simulate the exam experience, help you manage your time, and train you to recognize question patterns. This makes it easier to stay focused and improve your chances of passing on the first attempt.
Can I retake the exam if I do not pass the first time?
Retake policies are determined by Microsoft, so candidates should review the official exam rules before scheduling. The safest approach is to prepare thoroughly before your first attempt.
The questions for AI-901 were last updated on Aug 14, 2026.
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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?
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
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?
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.
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft's Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data. It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL.
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
The correct text analysis technique is Named Entity Recognition (NER).
Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations.
Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.
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?
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.
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