Prep Tests: Azure AI Engineer Associate Exam AI-102

Candidates for the Azure AI Engineer Associate certification build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services. 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 certification should be proficient in C# or Python 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. They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles. Skills measured Plan and manage an Azure Cognitive Services solution Implement Computer Vision solutions Implement natural language processing solutions Implement knowledge mining solutions Implement conversational AI solutions The Exam consists of questions covering the following modules/topics: - Plan and Manage an Azure Cognitive Services Solution (15-20%) 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 - Implement Computer Vision Solutions (20-25%) 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 Portal Implement an object detection solution by using the Custom Vision service Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer) - Implement Natural Language Processing Solutions (20-25%) 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 Knowledge Mining Solutions (15-20%) Implement a Cognitive Search solution Implement an enrichment pipeline Implement a knowledge store Manage a Cognitive Search solution Manage indexing - Implement Conversational AI Solutions (15-20%) 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