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Exam Code: AI-900
Exam Name: Microsoft Azure AI Fundamentals
Exam Q&As: 229 Q&As
Last update: May 27, 2024

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Introduce Microsoft AI-900 Exam

The Artificial intelligence (AI) will open doors into possibilities that might have seemed like science fiction only yesterday. You can master the basics in AI to help you jump-start your career and get ready to dive deeper into the other technical opportunities Azure offers. The Microsoft Azure AI Fundamentals AI-900 exam is the part of the requirements for Microsoft Certified: Azure AI Fundamentals certification. It can supply the foundation you need to build your career and demonstrate your knowledge of common AI and machine learning workloads—and what Azure services can solve for them. Also, it validates your foundational knowledge of machine learning and AI concepts, along with related Azure services. The Microsoft AI-900 exam is available in multiple languages, including English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian. Make sure that you can pass the AI-900 exam and earn the Microsoft Certified: Azure AI Fundamentals certification to promote your positions.

Learning AI-900 Exam Objectives Is Strongly Recommended

  • Identify features of anomaly detection workloads
  • Identify computer vision workloads
  • Identify natural language processing workloads
  • Identify knowledge mining workloads
  • Describe considerations for fairness in an AI solution
  • Describe considerations for reliability and safety in an AI solution
  • Describe considerations for privacy and security in an AI solution
  • Describe considerations for inclusiveness in an AI solution
  • Describe considerations for transparency in an AI solution
  • Describe considerations for accountability in an AI solution
  • Identify regression machine learning scenarios
  • Identify classification machine learning scenarios
  • Identify clustering machine learning scenarios
  • Identify features and labels in a dataset for machine learning
  • Describe how training and validation datasets are used in machine learning
  • Automated machine learning
  • Azure Machine Learning designer
  • Identify features of image classification solutions
  • Identify features of object detection solutions
  • Identify features of optical character recognition solutions
  • Identify features of facial detection and facial analysis solutions
  • Identify capabilities of the Computer Vision service
  • Identify capabilities of the Custom Vision service
  • Identify capabilities of the Face service
  • Identify capabilities of the Form Recognizer service
  • Identify features and uses for key phrase extraction
  • Identify features and uses for entity recognition
  • Identify features and uses for sentiment analysis
  • Identify features and uses for language modeling
  • Identify features and uses for speech recognition and synthesis
  • Identify features and uses for translation
  • Identify capabilities of the Language service
  • Identify capabilities of the Speech service
  • Identify capabilities of the Translator service
  • Identify features and uses for bots
  • Identify capabilities of the Power Virtual Agents and Azure Bot service

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