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Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement generative AI and agentic solutions	30–35%	- Build generative AI applications 1. Implement prompt engineering and optimization 2. Integrate Azure OpenAI and other models 3. Implement function calling and tool use 4. Build retrieval-augmented generation (RAG) solutions - Design and implement intelligent agents 1. Manage state, memory, and context 2. Integrate agents with external systems and data sources 3. Implement multi-agent workflows and orchestration 4. Select agent architecture patterns
Topic 2: Implement information extraction and knowledge mining	10–15%	- Build knowledge bases and search solutions 1. Implement Azure AI Search 2. Design knowledge mining pipelines 3. Create and manage vector indexes - Extract structured data from documents 1. Process forms, invoices, and unstructured content 2. Use Azure AI Document Intelligence
Topic 3: Implement text and speech analysis solutions	10–15%	- Implement speech capabilities 1. Speech-to-text and text-to-speech integration 2. Speech translation and speaker recognition - Implement natural language processing 1. Build conversational language understanding 2. Use Azure AI Language services 3. Perform sentiment analysis, entity recognition, and summarization
Topic 4: Implement computer vision solutions	10–15%	- Build multimodal solutions 1. Combine vision and language capabilities 2. Process and analyze video content - Implement image analysis and processing 1. Implement object detection and image classification 2. Use Azure AI Vision services 3. Extract text and structure from images
Topic 5: Plan and manage Azure AI solutions	25–30%	- Manage AI solution development lifecycle 1. Configure model and agent deployments 2. Integrate with CI/CD pipelines 3. Monitor and maintain AI workloads - Design Azure AI infrastructure 1. Design for scalability, availability, and cost optimization 2. Select appropriate Azure AI Foundry services 3. Plan for security, compliance, and responsible AI

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question #1
Drag and Drop Question
You have a Microsoft Foundry project that contains an agent. The agent uses threads and file uploads and calls an Azure OpenAI model deployment.
During load testing, calls intermittently fall and return an HTTP 429 rate limit exceeded error.
Some user uploads fail and generate an HTTP 400 file size exceeded error.
You need to mitigate the errors and reduce call failures. The solution must remain within the service and model limits.
What should you do to resolve each error? To answer, drag the appropriate actions to the correct errors. Each action may be used once, more than once or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each comet selection is worth one point.

Question #2
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?

A. the endpoint URI and an OAuth token
B. the endpoint URI and subscription key
C. a system assigned managed identity and an X.509 certificate
D. the endpoint URI and a shared access signature (SAS) token

Question #3
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?

A. Provision an Application Insights resource.
B. Add Agent1 to the Microsoft Foundry playground.
C. Add Agent1to a Microsoft Foundry project.
D. Create a Microsoft Foundry project.

Question #4
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support real-time inference. The solution must meet the following requirements:
- Use key-based authentication.
- Support real-time REST API access.
- NOT consume the vCPU quota of the virtual machines in the Azure
subscription.
Which type of deployment should you use?

A. standard
B. serverless API
C. self-hosted container
D. batch

Question #5
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search for Retrieval Augmented Generation (RAG).
You plan to ingest and index PDF product manuals.
You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Which indexing approach should you use?

A. vector search
B. semantic ranking
C. analyzers
D. suggesters

Solutions:

Question #1
Correct Answer: Only visible for members

Question #2
Correct Answer: B

Question #3
Correct Answer: D

Question #4
Correct Answer: A

Question #5
Correct Answer: A