Microsoft Azure AI Fundamentals (Updated Version) (AI-901) Exam Questions
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The Microsoft AI-901 exam is designed for professionals who want to improve their skills in Microsoft Azure AI Fundamentals (Updated Version) technology. This certification checks your understanding of the main concepts, tools, and best practices. It also tests your ability to use in real work situations.
The Microsoft Azure AI Fundamentals (Updated Version) certification can be a valuable step toward building a strong foundation for your IT career. It helps employers recognize your abilities and gives you a better chance of finding new job opportunities. This certification also helps you stay up-to-date with the latest Microsoft Azure AI Fundamentals (Updated Version) exam technologies, tools, and industry standards.
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Stand Out to Hiring Managers
The Microsoft AI-901 certification exam can help demonstrate your knowledge and skills, giving hiring managers a clearer view of your professional capabilities.
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The Microsoft AI-901 certification can help you demonstrate your skills, strengthen your professional profile, and prepare for new career opportunities.
Higher Job Security
A Microsoft Azure AI Fundamentals (Updated Version) credential raises your value, commands higher pay, and makes you hard to replace.
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Microsoft AI-901 Exam Domains & Weightage
Microsoft AI-901 Exam Details (Official)
| Vendor | Microsoft |
| Exam Code | AI-901 |
| Exam Name | Microsoft Azure AI Fundamentals (Updated Version) |
| Certification | Microsoft Azure |
Microsoft Azure AI Fundamentals (Updated Version) Exam Objectives
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1
1.0 Identify AI concepts and capabilities (40-45%)
- 1.1Describe principles of responsible AI
- 1.2Describe considerations for fairness in an AI solution
- 1.3Describe considerations for reliability and safety in an AI solution
- 1.4Describe considerations for privacy and security in an AI solution
- 1.5Describe considerations for inclusiveness in an AI solution
- 1.6Describe considerations for transparency in an AI solution
- 1.7Describe considerations for accountability in an AI solution
- 1.8Identify AI model components and configurations
- 1.9Describe how generative AI models work
- 1.10Identify an appropriate AI model, based on capabilities
- 1.11Identify appropriate model deployment options and configuration parameters
- 1.12Identify AI workloads
- 1.13Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
- 1.14Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization
- 1.15Identify features and capabilities of speech recognition and speech synthesis
- 1.16Identify features and capabilities of computer vision and image-generation models
- 1.17Identify techniques to extract information from text, images, audio, and videos
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2
2.0 Implement AI solutions by using Microsoft Foundry (55-60%)
- 2.1Implement generative AI apps and agents by using Foundry
- 2.2Create effective system and user prompts for generative AI models
- 2.3Deploy a model and interact with it in the Foundry portal
- 2.4Create a lightweight chat client application by using the Foundry SDK
- 2.5Create and test a single-agent solution in the Foundry portal
- 2.6Create a lightweight client application for an agent
- 2.7Implement AI solutions for text and speech by using Foundry
- 2.8Build a lightweight application that includes text analysis
- 2.9Respond to spoken prompts by using a deployed multimodal model
- 2.10Build a lightweight application by using Azure Speech in Foundry Tools
- 2.11Implement AI solutions with computer vision and image-generation capabilities by using Foundry
- 2.12Interpret visual input in prompts by using a deployed multimodal model
- 2.13Create new visual outputs by using generative models
- 2.14Build a lightweight application that includes vision capabilities
- 2.15Implement AI solutions for information extraction by using Foundry
- 2.16Extract information from documents and forms by using Azure Content Understanding in Foundry Tools
- 2.17Extract information from images by using Content Understanding
- 2.18Extract information from audio and video by using Content Understanding
- 2.19Build a lightweight application with information extraction capabilities by using Content Understanding
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