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 17, 2026
  • Q & A: 159 Questions and Answers

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Plan and manage Azure AI solutions25–30%- Design Azure AI infrastructure
  • 1. Plan for security, compliance, and responsible AI
  • 2. Design for scalability, availability, and cost optimization
  • 3. Select appropriate Azure AI Foundry services
- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Configure model and agent deployments
  • 3. Monitor and maintain AI workloads
Topic 2: Implement generative AI and agentic solutions30–35%- 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
- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Manage state, memory, and context
  • 3. Implement multi-agent workflows and orchestration
  • 4. Select agent architecture patterns
Topic 3: Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Build conversational language understanding
  • 2. Perform sentiment analysis, entity recognition, and summarization
  • 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 4: Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Extract text and structure from images
  • 3. Implement object detection and image classification
- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
Topic 5: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Implement Azure AI Search
  • 2. Create and manage vector indexes
  • 3. Design knowledge mining pipelines
- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1

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 #2

You are building a customer support web app named App1 in Microsoft Foundry that uses a GPT realtime model.
App1 must support:
- Live, low-latency voice conversations that use Azure OpenAI
- Streaming audio input from users and playback audio responses
You need to configure a connection method that supports real-time audio streaming in client application and targets approximately 100 ms latency.
Which connection method should you use?

  • A. SIP
  • B. WebSocket
  • C. RTMP
  • D. WebRTC
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support a search solution for internal policy documents. The model must generate vector representations of the text in the documents and of user queries.
Which type of model should you use?

  • A. a small language model (SLM)
  • B. a large language model (LLM)
  • C. an embedding model
  • D. an image generation model
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?

  • A. Set the input_fidelity parameter to high.
  • B. Include a prompt and input image in the request.
  • C. Apply a groundedness detection filter.
  • D. Decrease the value of the temperature parameter.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

You have a custom named entity recognition (NER) project in Azure Language in Foundry Tools for support tickets. The schema for the project contains an entity type named ContactInfo.
In tagged training files, ContactInfo is used for phone numbers, email addresses, and social media handles.
Model evaluation shows low precision for ContactInfo, including false positives in which nearby text is extracted as ContactInfo.
You need to improve the precision of the project.
What should you do before retraining the model?

  • A. Trigger an auto-labeling job.
  • B. Lower the confidence threshold for ContactInfo.
  • C. Replace ContactInfo by using Phone, Email, and SocialMedia entities. Relabel every matching span.
  • D. Add more support tickets as training data and label more ContactInfo entities.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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