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The preparation process for the exam is a vital step on your way of passing the test, that’s why you need to find the most actual and reliable resources. Microsoft offers two options for you to choose from online free training or instructor-led, which is a paid one. For example, the free training represents a collection of learning paths each of which contains the different number of modules, from one to five. Thus, you can choose which ones to follow: Prepare for AI engineering (1 module), Process and Translate Speech with Azure Cognitive Speech Services (2 modules), Create computer vision solutions with Azure Cognitive Services (3 modules), to name a few. The paid course is known to be Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution and lasts for 4 days. It is intended for software developers interested in developing skills to build AI infused apps that use Azure Cognitive Search and Services, and Microsoft Bot Framework.
In addition, you can check the Amazon website to find the books on the topics included in the exam to ace it from the first attempt. Only after the successful passing the AI-102 exam you will earn the Microsoft Certified: Azure AI Engineer Associate certification. So, good luck!
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 3: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 4: Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Extract text and handwriting from images - Analyze images and detect objects/features - Build and deploy custom vision models |
| Topic 5: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| Topic 6: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Deploy and manage generative models |
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Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
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