Prepare with Microsoft AI-102 exam practice material, pass for sure
Updated: Aug 06, 2026
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Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Exam Price | $165 (USD) |
| Schedule Exam | Pearson VUE |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Exam Code | AI-102 |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Duration | 130 mins |
Microsoft AI-102 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (scaled score out of 1000) |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals (AI-900) Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Developer Associate |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Exam Duration: | 100 minutes |
| Exam Format: | Multiple choice, Multiple select, Drag-and-drop, Case studies, Hot area, Performance-based scenarios |
| Available Languages: | English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Italian, Indonesian |
| Recommended Training: | AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Microsoft Learn Learning Paths for AI-102 |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-102 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 |
Exam Details
Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Build knowledge bases and search indexes |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types |
| Implement computer vision solutions | 10-15% | - Process and index video content - Extract text and handwriting from images - Integrate vision capabilities into applications - Analyze images and detect objects/features - Build and deploy custom vision models |
| Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - 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 appropriate Microsoft Foundry Services |
| Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots |
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
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