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For more info read reference:
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
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam
Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam
Introduction
Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.
This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.
For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.
Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.
In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
| 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 |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services |
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Integrate vision capabilities into applications - Extract text and handwriting from images - Process and index video content - Analyze images and detect objects/features |
