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Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Number of Questions | 40-60 |
| Exam Price | $165 (USD) |
| Schedule Exam | Pearson VUE |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Duration | 130 mins |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Passing Score | 700 / 1000 |
| Exam Code | AI-102 |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
To be eligible for this certification, you should have a good understanding of Azure fundamentals, including Azure Virtual Machines, Azure Storage, and Azure Virtual Networks. You should also have experience with machine learning models, including deep learning and natural language processing. Additionally, you should have knowledge of programming languages such as Python, R, and Scala.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Exam Format: | Hot area, Case studies, Multiple select, Multiple choice, Performance-based scenarios, Drag-and-drop |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100 minutes |
| Available Languages: | English, Japanese, Indonesian, Chinese (Simplified), French, Portuguese (Brazil), Korean, German, Spanish, Arabic (Saudi Arabia), Italian |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Passing Score: | 700 (scaled score out of 1000) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals (AI-900) Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Developer Associate |
| Recommended Training: | Microsoft Learn Learning Paths for AI-102 AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-102日本語 Sample Questions |
| Exam Way: | Online proctored (OnVUE) or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: experience with Azure services, AI concepts, and proficiency in Python or C#; AI-900 certification is recommended but not required |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102 |
Microsoft AI-102日本語 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 - Customize and deploy NLP models - Implement translation and summarization |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Select suitable AI models - Create and configure Azure AI resources |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models - Process and index video content - Analyze images and detect objects/features |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Deploy and manage generative models |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases |
By Merle
