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Passing the Microsoft AI-100: Designing and Implementing an Azure AI Solution exam is required for anyone who wants to earn the Microsoft Certified: Azure AI Engineer Associate certification. This test evaluates the ability of the candidates to perform the following technical tasks: analyzing solution requirements; designing solutions; integrating AI models into solutions; and deploying and handling solutions.
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Designing and Implementing an Azure AI Solution AI-100 Exam
Designing and Implementing an Azure AI Solution AI-100 Exam which is related to Microsoft Azure AI Solution and credits toward Microsoft Certified Azure AI Engineer Associate certifications. This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio. Candidate must have a strong understanding of basic statistics, data ethics, data privacy, various aspects of the Microsoft Azure AI portfolio, related open source frameworks and technologies, and available data storage options. This exam verifies the Candidates have the ability to use their knowledge of cost models, capacity, and best practices to architect and implement Artificial Intelligence (AI) Solution using Microsoft Azure.
Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx
Microsoft AI-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analyze Solution Requirements | 25-30% | - Define solution performance and scalability criteria - Assess security, compliance, and governance needs - Evaluate data storage and processing options - Identify business and technical requirements |
| Topic 2: Design AI Solutions | 40-45% | - Design conversational AI and bot solutions - Integrate open-source and custom AI components - Design knowledge mining and search solutions - Select appropriate Azure Cognitive Services - Design natural language processing solutions - Design computer vision solutions |
| Topic 3: Implement and Monitor AI Solutions | 25-30% | - Configure security and access controls - Integrate AI models and services into applications - Maintain and update AI solutions - Deploy AI solutions to Azure infrastructure - Monitor performance, usage, and health |




