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Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement generative AI solutions	25-30%	- Optimize and evaluate models 1. Implement multimodal AI capabilities 2. Configure content filters and safety 3. Evaluate responses and grounding - Develop generative AI applications 1. Use Azure OpenAI and Foundry models 2. Build retrieval-augmented generation solutions 3. Implement prompt engineering
Topic 2: Implement agentic solutions	20-25%	- Manage agent operations 1. Secure agent interactions 2. Monitor and debug agents 3. Implement scalable deployments - Build AI agents 1. Create autonomous and multi-agent workflows 2. Integrate tools and external knowledge 3. Configure memory and orchestration
Topic 3: Implement text analysis and information extraction solutions	10-15%	- Analyze and extract information 1. Use document intelligence services 2. Implement natural language processing 3. Extract entities and structured data
Topic 4: Implement computer vision solutions	10-15%	- Analyze visual content 1. Process images and video 2. Use multimodal vision APIs 3. Implement OCR and visual understanding
Topic 5: Plan and manage Azure AI solutions	25-30%	- Manage AI solution lifecycle 1. Implement CI/CD for AI applications 2. Apply responsible AI practices 3. Monitor model and application performance - Plan Azure AI resources 1. Manage deployments and monitoring 2. Select Azure AI services and Foundry resources 3. Configure authentication and security

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?

A) an Azure Machine Learning model
B) Azure Content Understanding in Foundry Tools
C) Azure Language in Foundry Tools

2. You have an Azure subscription that contains an Azure OpenAI resource.
You deploy the GPT-4 model to the resource.
You need to ensure that you can upload files that will be used as grounding data for the model.
Which two types of resources should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Azure SQL
B) Azure AI Bot Service
C) Azure AI Search
D) Azure Blob Storage
E) Azure AI Document Intelligence

3. You have a large collection of image files and PDF documents stored in an Azure Storage account. The documents contain tabular data.
You need to extract the tables into a structured format that can be imported into a database. The solution must minimize development effort. What should you use?

A) Azure Content Understanding in Foundry Tools
B) Azure Vision in Foundry Tools
C) Azure AI Video Indexer
D) Azure Document Intelligence in Foundry Tools

4. You are developing a new sales system that will process user-generated video and text from a public-facing website.
You plan to notify users that their data has been processed by the sales system.
Which responsible AI principle does this help meet?

A) reliability and safety
B) transparency
C) inclusiveness
D) fairness

5. Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.