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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Certificate Validity Period:Does not expire (Fundamentals certification)
Exam Format:Drag and drop, Multiple choice, Multiple response, Case study (limited)
Exam Duration:60 minutes
Related Certifications:Microsoft Azure Fundamentals (AZ-900)
Microsoft Azure Data Fundamentals (DP-900)
Exam Price:Approx. 99 USD (varies by region)
Available Languages:Chinese (Traditional), English, Korean, German, French, Chinese (Simplified), Spanish, Portuguese (Brazil), Japanese
Passing Score:700 (out of 1000)
Real Exam Qty:40-60
Recommended Training:Microsoft Learn - AI-900 Learning Path
Azure AI Fundamentals Course
Exam Registration:Microsoft Certification Portal
Schedule exam via Pearson VUE
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.

Microsoft Azure AI Fundamentals Sample Questions (Q68-Q73):

NEW QUESTION # 68
You deploy an AI system to assist with hiring decisions.
Company policy requires that human reviewers oversee AI-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible AI principle is this an example of?

Answer: A

Explanation:
Accountability is the Microsoft responsible AI principle that ensures human oversight so that people remain responsible for AI-driven decisions.
Human Oversight: Requiring human review ensures that AI operates under effective governance rather than acting autonomously on high-impact choices.
Final Authority: Keeping humans legally and operationally responsible prevents automated systems from dictating critical outcomes like hiring.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach


NEW QUESTION # 69
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 70
Drag and Drop Question
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.

Answer:

Explanation:


NEW QUESTION # 71
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 72
Select the answer that correctly complete the sentence.

Answer:

Explanation:

Explanation:

The Model catalog is used for comparing and deploying a wide range of models for generative AI development in Microsoft Foundry.
Microsoft documentation states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications . It also includes many models across providers such as Azure OpenAI, Mistral, Meta, Cohere, NVIDIA, and Hugging Face.
The Microsoft Learn module for Foundry models also states that the model catalog is used to explore and filter models , compare models using benchmark metrics , and deploy a model to an endpoint .


NEW QUESTION # 73
......

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