Microsoft AI-103 : Developing AI Apps and Agents on Azure

Exam Code: AI-103
Exam Name: Developing AI Apps and Agents on Azure
Updated: Sep 18, 2026 Q & A: 159 Questions and Answers

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

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1

You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?

A. Cognitive Services User
B. Cognitive Services Data Reader
C. Cognitive Services OpenAI User
D. Contributor

Reveal Solution Discussion 0

Correct Answer: C 🗳️