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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 2: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 3: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 4: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 5: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
- Some requests take more than 15 seconds to complete.
- Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
A) tracing
B) token usage
C) safety metrics
D) monitoring
2. You have an Azure AI Search indexer that ingest PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
A) Azure Content Understanding
B) Document Layout
C) Document Extraction
D) GenAI Prompt
3. A developer needs a managed identity to call a Microsoft Foundry project endpoint to run inference and use agents. Security policy requires the least-privilege role that grants data-plane access and nothing more. Which built-in role should you assign to the managed identity?
A) Cognitive Services Contributor
B) Azure AI Developer
C) Foundry Account Owner
D) Foundry User
4. Hotspot Question
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
5. You are planning a Microsoft Foundry project named Project1 that will contain multiple agents.
Each agent will access the same Azure AI Search resource.
You need to recommend a solution to centrally manage the Azure AI Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
A) Create a managed private endpoint that connects to the Azure AI Search resource.
B) Disable key-based access control on the Azure AI Search resource.
C) Add a connection to the Azure AI Search resource.
D) Enable role-based access control (RBAC) for the Azure AI Search resource.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: Only visible for members | Question # 5 Answer: C |

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