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Updated: Sep 19, 2026

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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
Exam Format:Scenario-based, Multiple choice, Performance-based items, Case study
Real Exam Qty:50–60
Passing Score:700 / 1000
Related Certifications:Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026)
Exam Duration:100 minutes
Available Languages:Korean, Chinese (Simplified), French, Spanish, Portuguese (Brazil), Japanese, German, English
Certificate Validity Period:1 year (renewable annually via free online assessment)
Exam Price:$165 USD (varies by country/region)
Recommended Training:Azure AI Foundry Documentation
Microsoft Learn: Develop AI Apps and Agents on Azure
Exam Registration:Pearson VUE Registration
Microsoft Certification Dashboard
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:

SectionWeightObjectives
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Configure model and agent deployments
  • 3. Integrate with CI/CD pipelines
- 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
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Select agent architecture patterns
  • 3. Manage state, memory, and context
  • 4. Implement multi-agent workflows and orchestration
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Implement function calling and tool use
  • 3. Integrate Azure OpenAI and other models
  • 4. Build retrieval-augmented generation (RAG) solutions
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Implement object detection and image classification
  • 3. Extract text and structure from images
Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Create and manage vector indexes
  • 2. Implement Azure AI Search
  • 3. Design knowledge mining pipelines
- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content
Implement text and speech analysis solutions10–15%- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
- Implement natural language processing
  • 1. Build conversational language understanding
  • 2. Perform sentiment analysis, entity recognition, and summarization
  • 3. Use Azure AI Language services

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 OpenAI User
  • B. Cognitive Services Data Reader
  • C. Contributor
  • D. Cognitive Services User
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

You have a Microsoft Foundry project that contains an agent.
The agent uses Azure Content Understanding in Foundry Too to process vendor onboarding packets. The packs include digital PDFs that contain tables and hyperlinks.
The extracted content is indexed for search and provided to a downstream agent in the Markdown format.
You need to generate a Markdown output that has a layout and a semantic structure optimized for Retrieval Augmented Generation (RAG) workflows.
Which built-in analyzer should you use?

  • A. prebuilt-documentFieldSchema
  • B. prebuilt-documentSearch
  • C. prebuilt-layout
  • D. prebuilt-read
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?

  • A. image moderation
  • B. protected material text
  • C. prompt shields for documents
  • D. prompt shields for user prompts
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor's logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?

  • A. Increase the prompt guidance strength.
  • B. Rerun the prompt by using a different random seed.
  • C. Apply a mask-based inpainting edit to the part of the image that contains the logo.
  • D. Modify the original prompt to exclude brand names.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

Drag and Drop Question
You have a Microsoft Foundry project that contains an agent.
You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Box 1: "{{$userId}}"
User Isolation (Scope)
Setting scope="{{$userId}}" instructs the Microsoft Foundry Agent Service to automatically map and partition memory to the logged-in individual using their Microsoft Entra user identity template.
The platform handles this dynamically from request headers, removing the need for manual client-side user ID management Box 2: [memory_tool] Tool List: The tools configuration parameter expects an iterable containing instantiated tool objects. Wrapping memory_tool inside a Python list ([memory_tool]) exposes the capability to the agent definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/memory-usage?pivots=python

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