AI-102: Designing and Implementing a Microsoft Azure AI Solution

About Microsoft Certified: Microsoft Azure AI Engineer Associate

The Microsoft Certified: Candidates for Exam AI-102 – “Designing and Implementing a Microsoft Azure AI Solution” build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring. They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions. Candidates for this exam should be proficient in C#, Python, or JavaScript and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure. This exam measures your ability to accomplish the following technical tasks:

• Plan and manage an Azure Cognitive Services solution (15-20%)
• Implement Computer Vision solutions (20-25%)
• Implement natural language processing solutions (20-25%)
• Implement knowledge mining solutions (15-20%)
• Implement conversational AI solutions (15-20%)

Recommended Knowledge                                                                   

• Select the appropriate Cognitive Services resource
• Plan and configure security for a Cognitive Services solution
• Create a Cognitive Services resource
• Plan and implement Cognitive Services containers
• Analyze images by using the Computer Vision API
• Extract text from images
• Extract facial information from images
• Implement image classification by using the Custom Vision service
• Implement an object detection solution by using the Custom Vision service
• Analyze video by using Video Indexer
• Analyze text by using the Text Analytics service
• Manage speech by using the Speech service
• Translate language
• Build an initial language model by using Language Understanding Service (LUIS)
• Iterate on and optimize a language model by using LUIS
• Manage a LUIS model
• Implement a Cognitive Search solution
• Implement an enrichment pipeline
• Implement a knowledge store
• Manage a Cognitive Search solution
• Manage indexing
• Create a knowledge base by using QnA Maker
• Design and implement conversation flow
• Create a bot by using the Bot Framework SDK
• Create a bot by using the Bot Framework Composer
• Integrate Cognitive Services into a bot