Microsoft AI-103 Exam Details & Actual Exam Questions

Exam Code/Number: AI-103
Exam Name/Title: Developing AI Apps and Agents on Azure
Certification Provider: Microsoft
Corresponding Certification: Azure AI Engineer Associate

Exam Questions: 159
Updated On: Sep,10 2026
Certification Level: Associate

Microsoft Developing AI Apps and Agents on Azure Exam Questions

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Microsoft AI-103 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Developing AI Apps and Agents on Azure
Exam Number:	AI-103
Certificate Validity Period:	1 year (renewable annually via free online assessment)
Real Exam Qty:	50–60
Exam Duration:	100 minutes
Related Certifications:	Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026)
Passing Score:	700 / 1000
Exam Format:	Performance-based items, Case study, Scenario-based, Multiple choice
Available Languages:	Japanese, French, Spanish, Portuguese (Brazil), Korean, Chinese (Simplified), German, English
Exam Price:	$165 USD (varies by country/region)
Recommended Training:	Microsoft Learn: Develop AI Apps and Agents on Azure Azure AI Foundry Documentation
Exam Registration:	Microsoft Certification Dashboard Pearson VUE Registration
Sample Questions:	Microsoft AI-103 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103

Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement generative AI and agentic solutions	30–35%	- Design and implement intelligent agents 1. Select agent architecture patterns 2. Manage state, memory, and context 3. Integrate agents with external systems and data sources 4. Implement multi-agent workflows and orchestration - Build generative AI applications 1. Build retrieval-augmented generation (RAG) solutions 2. Implement prompt engineering and optimization 3. Implement function calling and tool use 4. Integrate Azure OpenAI and other models
Topic 2: Implement computer vision solutions	10–15%	- Build multimodal solutions 1. Process and analyze video content 2. Combine vision and language capabilities - Implement image analysis and processing 1. Implement object detection and image classification 2. Extract text and structure from images 3. Use Azure AI Vision services
Topic 3: Plan and manage Azure AI solutions	25–30%	- Manage AI solution development lifecycle 1. Configure model and agent deployments 2. Integrate with CI/CD pipelines 3. Monitor and maintain AI workloads - Design Azure AI infrastructure 1. Select appropriate Azure AI Foundry services 2. Plan for security, compliance, and responsible AI 3. Design for scalability, availability, and cost optimization
Topic 4: Implement text and speech analysis solutions	10–15%	- Implement speech capabilities 1. Speech-to-text and text-to-speech integration 2. Speech translation and speaker recognition - Implement natural language processing 1. Build conversational language understanding 2. Perform sentiment analysis, entity recognition, and summarization 3. Use Azure AI Language services
Topic 5: Implement information extraction and knowledge mining	10–15%	- Build knowledge bases and search solutions 1. Design knowledge mining pipelines 2. Create and manage vector indexes 3. Implement Azure AI Search - Extract structured data from documents 1. Use Azure AI Document Intelligence 2. Process forms, invoices, and unstructured content