Pass test with the help of AI-102 Korean actual exam materials
Last Updated: Aug 01, 2026
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Exam AI-102 contains five topics each of which is intended to check specific skills.
1. Plan and Manage an Azure Cognitive Services Solution
This topic implies your ability to choose the suitable Cognitive Services resource, create it, plan and design security for a Cognitive Services solution, and apply Cognitive Services containers. This means that you should be competent in selecting the appropriate cognitive service for solutions that refer to language analysis, speech, decision support, and vision. You also should possess skills to operate costs of Cognitive Services, create a Cognitive Services resource, and monitor a cognitive service. This part also checks how well you can operate Cognitive Services account keys, and protect Cognitive Services. Your knowledge of using Face API, Computer Vision, Speech, Text Analysis, and ability to integrate Cognitive Services Containers in Microsoft Azure will also be assessed.
2. Implement Computer Vision Solutions
The second topic is designed to check your skills in using the Computer Vision API to get image descriptions, define landmarks, find brands, edit content in images, and create thumbnails. In this part, you are expected to be able to detect faces and recognize them in images, analyze facial features, and match similar faces with the help of the Face API. Being competent in utilizing the Custom Vision service, you should demonstrate your skills in applying image classification and implementing an object detection solution. Besides, your ability to analyze video by implementing Azure Video Analyzer for Media will be measured.
3. Implement Natural Language Processing Solutions
In the third topic, candidates are required to show their skills in analyzing text by utilizing the Text Analytics service, control speech by implementing the Speech service, translate the text with the help of the Translator service. This domain also checks your proficiency in creating and optimizing an initial language model by utilizing LUIS, and finally, managing it.
4. Implement Knowledge Mining Solutions
In this domain, you will be required to have expertise related to applying a Cognitive Search solution, which implies creating data sources, identifying an index, running an indexer, and using synonyms. This topic also aims to evaluate your ability to apply an enrichment pipeline, use a knowledge store, operate a Cognitive Search solution and indexing.
5. Implement Conversational AI Solutions
This domain will evaluate your capacity in utilizing QnA Maker to make a knowledge base, creating and implementing conversation flow, creating a bot by utilizing either the Bot Framework Composer or the Bot Framework SDK. Finally, you will need to demonstrate your skills in integrating Cognitive Services into a bot.
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
| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Extract text and handwriting from images - Integrate vision capabilities into applications - Build and deploy custom vision models - Process and index video content |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Select suitable AI models |
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Orchestrate multiple models and containers - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection |
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