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Prep Options to Choose
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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Skills measured
- Implement conversational AI solutions (15-20%)
- Plan and manage an Azure Cognitive Services solution (15-20%)
- Implement knowledge mining solutions (15-20%)
- Implement natural language processing solutions (20-25%)
- Implement Computer Vision solutions (20-25%)
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AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary
The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
- Europe - 90,547 EURO
- England - 90,532 POUND
- India - 20,00,327 INR
- United State - 120,000 USD
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents |
| Topic 2: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Implement intelligent search and retrieval |
| Topic 4: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization |
| Topic 5: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images - Process and index video content |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles |
