The AI-103: Developing AI Apps and Agents on Azure exam is designed for Azure AI engineers who build, manage, and deploy AI applications and agents using Microsoft Foundry and Azure services. Candidates are expected to have experience developing applications with Python and understand general AI, generative AI, and Azure services.
What Is the AI-103 Certification?
Passing AI-103 earns the Microsoft Certified: Azure AI Apps and Agents Developer Associate certification. It validates skills in planning, implementing, deploying, and managing modern Azure AI solutions and agentic applications.
Candidate Profile
AI-103 is suitable for professionals such as:
- Azure AI Engineers
- AI Application Developers
- Python Developers
- AI Agent Developers
- Cloud Developers
- Generative AI Developers
AI-103 Exam Topics
According to the current Microsoft AI-103 study guide, the major skill areas are:
| Skill Area | Weight |
|---|
| Plan and manage an Azure AI solution | 25–30% |
| Implement generative AI and agentic solutions | 30–35% |
| Implement computer vision solutions | 10–15% |
| Implement text analysis solutions | 10–15% |
| Implement information extraction solutions | 10–15% |
1. Plan and Manage an Azure AI Solution
This area focuses on selecting and configuring the appropriate services for AI applications.
Important topics include:
- Microsoft Foundry services
- Model selection
- LLMs and small language models
- Multimodal models
- Vector search
- Grounding
- Agent workflows
- Knowledge integration
- Azure AI infrastructure
- Model deployment
- CI/CD integration
Candidates should also understand how to manage:
- Scaling
- Rate limits
- Quotas
- Cost
- Model performance
- Safety events
- Security
Microsoft also includes responsible AI, safety filters, guardrails, risk detection, auditing, and agent oversight in this area.
2. Implement Generative AI and Agentic Solutions
This is the largest weighted domain in the AI-103 exam.
Generative AI Applications
Study:
- Deploying language models
- Small language models
- Multimodal models
- Model consumption
- Generative AI workflows
- RAG applications
- Prompt engineering
- Model evaluation
A common AI architecture may look like:
User → Application → Retrieval → AI Model → Response
AI Agents
AI agents can perform tasks by combining models, tools, knowledge, memory, and external services.
Important topics include:
- Agent roles and goals
- Tool schemas
- Conversation tracking
- Agent memory
- Function calling
- Knowledge integration
- APIs
- Custom functions
- Multi-agent orchestration
Microsoft's AI-103 study guide specifically includes building agents that integrate retrieval, function calling, conversation memory, tools, and orchestrated multi-agent solutions.
RAG Solutions
Retrieval-Augmented Generation (RAG) is an important concept for grounding AI responses with relevant information.
A simplified RAG flow is:
User Query → Search/Retrieval → Relevant Data → AI Model → Grounded Response
Study:
- Vector search
- Semantic search
- Hybrid search
- Indexing
- Knowledge stores
- Grounding
- Retrieval quality
3. Optimize and Operationalize AI Systems
Candidates should understand how to improve AI application performance and manage production workloads.
Study:
- Prompt engineering
- Model parameters
- AI output quality
- Evaluation
- Safety testing
- Relevance
- Fabrication detection
- Structured JSON outputs
- Monitoring
AI solutions should be continuously evaluated for quality, safety, relevance, and performance.
4. Implement Computer Vision Solutions
This domain focuses on building AI solutions that process visual information.
Important concepts include:
- Image analysis
- Visual understanding
- Multimodal AI
- Image processing
- Computer vision applications
Candidates should understand how AI services can analyze and extract useful information from images and other visual content.
5. Implement Text Analysis Solutions
AI-103 also includes working with text and language.
Study areas include:
- Text analysis
- Sentiment detection
- Topic extraction
- Entity extraction
- Summarization
- Translation
- Sensitive content detection
The exam also covers using generative AI and Azure services for language-based solutions.
6. Implement Information Extraction Solutions
Information extraction focuses on retrieving structured information from unstructured content.
Study:
- Document ingestion
- OCR
- Content Understanding
- Layout analysis
- Field extraction
- Structured outputs
- Document processing
- Retrieval pipelines
The current study guide includes ingesting and indexing documents, images, audio, and video, along with semantic, hybrid, and vector search for grounding.
AI-103 Preparation Strategy
Step 1: Learn Microsoft Foundry
Understand:
- Projects
- Models
- Model deployment
- Agents
- Evaluation
- AI tools
Microsoft Foundry is central to the AI-103 certification.
Step 2: Strengthen Your Python Skills
Candidates should have experience developing AI applications with Python.
Practice:
- API calls
- SDKs
- Data processing
- AI integrations
- Application development
Step 3: Build a RAG Application
Create a practice project that:
- Accepts a user question.
- Retrieves relevant information.
- Provides context to an AI model.
- Generates a grounded response.
Step 4: Build an AI Agent
Practice building an agent that can:
- Answer questions
- Use tools
- Call APIs
- Retrieve knowledge
- Perform actions
- Maintain conversation context
Step 5: Study Responsible AI
Focus on:
- Guardrails
- Safety filters
- Risk detection
- Content moderation
- Auditing
- Human oversight
Step 6: Practice Information Extraction
Work with:
- Documents
- OCR
- Structured information
- Search indexes
- RAG ingestion pipelines
AI-103 Study Plan
Week 1: Azure AI and Foundry
Study:
- Microsoft Foundry
- Azure AI infrastructure
- Models
- Deployments
- Security
Week 2: Generative AI
Focus on:
- LLMs
- Prompt engineering
- RAG
- Model evaluation
Week 3: AI Agents
Study:
- Agent roles
- Tools
- APIs
- Function calling
- Memory
- Multi-agent solutions
Week 4: Vision and Text
Focus on:
- Computer vision
- Text analysis
- Sentiment
- Summarization
- Translation
Week 5: Information Extraction
Study:
- OCR
- Document processing
- Retrieval
- Search
- Grounding
Week 6: Practice and Revision
Focus on:
- Scenario-based questions
- Practice projects
- Weak topics
- Evaluation and troubleshooting
AI-103 Practice Questions
Question 1
Which approach is most suitable for providing an AI application with relevant private documents?
A. RAG
B. Static HTML
C. DNS configuration
D. Image compression
Answer: A. RAG
RAG retrieves relevant information and provides it as context for AI-generated responses.
Question 2
Which component enables an AI agent to interact with an external service?
A. Tool or API integration
B. Static image
C. Database backup
D. Screen theme
Answer: A. Tool or API integration
Question 3
What is an important consideration when deploying a production AI application?
A. Monitoring performance and cost
B. Only changing the UI
C. Removing security
D. Disabling evaluation
Answer: A. Monitoring performance and cost
Question 4
Which technology can help extract text from scanned documents?
A. OCR
B. CSS
C. DNS
D. HTML
Answer: A. OCR
Question 5
Why are AI safety guardrails important?
A. They help manage risks and control AI behavior
B. They increase monitor resolution
C. They replace databases
D. They remove all application testing
Answer: A. They help manage risks and control AI behavior
AI-103 FAQs
What is AI-103?
AI-103 is Microsoft's exam for Developing AI Apps and Agents on Azure.
What certification do you earn by passing AI-103?
Passing AI-103 earns the Microsoft Certified: Azure AI Apps and Agents Developer Associate certification.
What technologies should I study for AI-103?
Focus on:
- Microsoft Foundry
- Azure AI services
- Python
- Generative AI
- AI agents
- RAG
- Computer vision
- Text analysis
- Information extraction
Does AI-103 cover AI agents?
Yes. Agent development, tool integration, memory, retrieval, function calling, and multi-agent solutions are included in the current skills measured.
Does AI-103 cover RAG?
Yes. RAG, grounding, retrieval, vector search, and indexing are important areas of the exam.
How long is the AI-103 exam?
Microsoft currently lists 120 minutes for completing the assessment.
Conclusion
The AI-103 Exam Guide: Developing AI Apps and Agents on Azure covers modern AI development concepts, including:
- Azure AI solutions
- Microsoft Foundry
- Generative AI
- AI agents
- RAG
- Model evaluation
- Computer vision
- Text analysis
- Information extraction
- Responsible AI
For effective preparation, combine Microsoft Learn resources with hands-on practice. Build AI applications and agents, experiment with retrieval and grounding, and practice evaluating production AI systems.
Written By:Sudheer Kumar
Published on: 07/09/2026
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