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Exam AI-100: Designing and Implementing an Azure AI Solution

THIS EXAM RETIRED ON JUNE 30, 2021. A replacement exam, AI-102, is available. For more information, visit the AI-102 exam details page.

Candidates for this exam should have subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.

Responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions, recommending the appropriate tools and technologies, and designing and implementing AI solutions that meet scalability and performance requirements.

Azure AI Engineers translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and software developers to build complete end-to-end solutions.

A candidate for this exam should have knowledge and experience designing and implementing AI apps and agents that use Microsoft Azure Cognitive Services, Azure Bot Service, Azure Cognitive Search, and data storage in Azure. In addition, a candidate should be able to recommend solutions that use open source technologies, understand the components that make up the Azure AI portfolio and the available data storage options, and understand when a custom API should be developed to meet specific requirements.

Part of the requirements for: none

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Before you decide to take the Microsoft AI-100 exam, it is important that you understand all the knowledge requirements for this test. The exam is not designed for the general public, so you need to possess certain competence before attempting it. The target candidates for this certification test should have the following skills:

  • Understanding when a custom API should be developed to satisfy certain business requirements.
  • Ability to put forward solutions that utilize open source technologies with understanding the elements that make up the Azure AI portfolio along with the available data storage options.
  • Expertise in implementing and designing AI applications and agents that utilize Microsoft Azure Cognitive Services.
  • Familiarity with data storage in Azure, Azure Cognitive Search, Microsoft Azure Cognitive Services, and Azure Bot Service.

Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx

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Microsoft AI-100日本語 Exam Syllabus Topics:

SectionObjectives
Implement Knowledge Mining and Search Solutions- Azure Cognitive Search
  • 1. Indexing and querying data
    • 2. Skillsets and enrichment pipeline
      Implement Natural Language Processing (NLP)- Text analytics solutions
      • 1. Key phrase extraction
        • 2. Sentiment analysis
          - Language understanding solutions
          • 1. Intent recognition
            • 2. Entity extraction
              Design AI Solutions on Azure- Design conversational AI solutions
              • 1. Azure Bot Service architecture
                • 2. Language understanding (LUIS concepts)
                  - Select appropriate Azure AI services
                  • 1. Azure Machine Learning integration
                    • 2. Azure Cognitive Services selection
                      • 3. Knowledge mining solutions
                        Implement Computer Vision Solutions- Image analysis and processing
                        • 1. Computer Vision API usage
                          • 2. Custom Vision training and deployment
                            Responsible AI and Monitoring- AI ethics and governance
                            • 1. Responsible AI principles
                              • 2. Model monitoring and evaluation

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