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 15, 2026
  • Q & A: 159 Questions and Answers

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content
- 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
Implement text and speech analysis solutions10–15%- 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
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. Design for scalability, availability, and cost optimization
  • 2. Plan for security, compliance, and responsible AI
  • 3. Select appropriate Azure AI Foundry services
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
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Implement multi-agent workflows and orchestration
  • 3. Integrate agents with external systems and data sources
  • 4. Manage state, memory, and context
- Build generative AI applications
  • 1. Implement function calling and tool use
  • 2. Build retrieval-augmented generation (RAG) solutions
  • 3. Implement prompt engineering and optimization
  • 4. Integrate Azure OpenAI and other models

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1

A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?

  • A. Disabling the agent's file search tool
  • B. Prompt Shields, which detects document and indirect prompt injection
  • C. Raising the image resolution threshold on the analyser
  • D. A content filter set to High severity for the Violence category
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Microsoft Sentinel
  • B. Application Insights
  • C. OpenTelemetry
  • D. a Log Analytics workspace
  • E. the Azure Monitor Agent
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

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Question #3

You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?

  • A. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
  • B. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
  • C. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
  • D. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

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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Question #5

You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. YAML
  • B. Semantic Kernel
  • C. Liquid
  • D. Handlebars
  • E. JSONL
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

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