Updated: Sep 07, 2026
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Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Real Exam Qty: | 40-60 |
| Available Languages: | Arabic (Saudi Arabia), Italian, Spanish, English, Portuguese (Brazil), Indonesian, Japanese, French, German, Chinese (Simplified), Korean |
| Exam Format: | Case studies, Multiple choice, Hot area, Multiple select, Drag-and-drop, Performance-based scenarios |
| Passing Score: | 700 (scaled score out of 1000) |
| Related Certifications: | Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure AI Fundamentals (AI-900) |
| Exam Duration: | 100 minutes |
| 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 |
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
| 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% | - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Integrate vision capabilities into applications - Build and deploy custom vision models - Extract text and handwriting from images - Process and index video content |
| 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 |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization |
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