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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Drag and Drop Question
You have a web app that uses Azure AI Search.
When reviewing activity you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:

Explanation:
Enforces Read-Only Permissions: Query keys are specifically designed to provide read-only access to the documents collection of an index. Admin keys provide full read-write administrative privileges and should never be distributed to consumer-facing applications.
Zero Downtime: Azure AI Search lets you generate up to 50 individual query keys. Creating a new one allows the app to stay online throughout the entire key rotation process Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-api-keys
You have an Azure subscription that contains an Azure OpenAI resource named AI1.
You build a chatbot that uses AI1 to provide generative answers to specific questions.
You need to ensure that questions intended to circumvent built-in safety features are blocked.
Which Azure AI Content Safety feature should you implement?
- A. Protected material text detection
- B. Monitor online activity
- C. Prompt Shields
- D. Moderate text content
Correct Answer: C 🗳️
Explanation: Only visible for DumpsMaterials members. You can sign-up / login (it's free).
Drag and Drop Question
You have a Microsoft Foundry project that contains a multi-agent solution. The agents use tool calling to query internal systems.
You need to implement responsible AI auditing to meet the following requirements:
- Capture all the nested operations across the entire agent run.
- Record tool invocation arguments and retuned results as metadata.
What should you use for each requirement? To answer, drag the appropriate options to the correct targets Each option may be used once, more than once, or not at all. You may need o dag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
Box 1: Hierarchical spans
Hierarchical spans should be used in this case.
Parent-Child tracking: In OpenTelemetry and Azure Monitor Application Insights (which back Microsoft Foundry environments), hierarchical spans utilize a unique Trace ID for the entire request and distinct Span IDs for individual operations.
Nested capture: When a primary agent calls a sub-agent, or an agent invokes a specific system tool, each subsequent operation is recorded as a child span. This explicitly preserves the parent- child relationship, allowing auditors to reconstruct the exact execution tree of all nested operations across the entire multi-agent run Box 2: Tool call attributes To record tool invocation arguments and returned results as metadata for responsible AI auditing in a Microsoft Foundry multi-agent project, you should use Tool call attributes.
In GenAI and multi-agent systems leveraging OpenTelemetry semantic conventions (which Microsoft Foundry utilizes for its observability, tracing, and logging pillars), tool execution details are captured via specific span attributes. Configured tool call attributes explicitly map and record the parameters (tool.call.arguments) and outputs (tool.call.result) as metadata keys inside the tracing context for auditing and validation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/observability/concepts/trace-data
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ai-observability-starter-kit-for-microsoft-foundry-agents/4522751
Hotspot Question
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:

Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to configure Agent1 to answer customer questions about only the Contoso products.
The solution must meet the business requirements. What should you do?
- A. Add few-shot examples.
- B. Apply top-p sampling.
- C. Increase the value of the temperature parameter.
- D. Modify the system message instructions.
Correct Answer: D 🗳️
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