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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Type of Questions: This test format is multiple choice.
- Passing Score: 700 / 1000
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
- Language: English, Japanese, Chinese (Simplified), Korean.
- This is beta exam.
- Length of Examination: 50 mins
For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.
Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Topic 2: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection |
| Topic 3: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Build and deploy custom vision models - Process and index video content - Analyze images and detect objects/features - Extract text and handwriting from images |
| Topic 4: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| Topic 5: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |








