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Microsoft AI-103 Exam Syllabus Topics:

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

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?

A. Azure OpenAI text classification
B. Azure AI Language custom named entity recognition (NER)
C. Azure OpenAI text summarization
D. Azure AI Language custom text classification

Question 2

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

Question 3

You are developing an application that will use Azure AI Search for internal documents.
You need to implement document-level filtering for Azure AI Search.
Which three actions should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Supply the groups as a filter for the search requests
B. Add allowed groups to each index entry
C. Retrieve the group memberships of the user
D. Create one index per group.
E. Retrieve all the groups.
F. Send access tokens from Microsoft Entra ID, with the search request.

Question 4

You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?

A. Add a retry evaluation before the responses are returned.
B. Replace the model with a smaller deployment.
C. Decrease the value of the temperature parameter.
D. Increase the value of the presence_penalty parameter

Question 5

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. prompt shields for documents
C. protected material text
D. prompt shields for user prompts

Solutions:

Question 1
Answer: A

Question 2
Answer: B

Question 3
Answer: A,B,C

Question 4
Answer: A

Question 5
Answer: B