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Microsoft AI-103

AI-103

Exam Code: AI-103

Exam Name: Developing AI Apps and Agents on Azure

Updated: Sep 16, 2026

Q & A: 159 Questions and Answers

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AI-103 exam dumps

Microsoft AI-103 Exam Syllabus Topics:

SectionObjectives
Implement Natural Language Processing Solutions- Language understanding and intent recognition
- Text analytics and summarization
- Translation and multilingual support
Plan and Manage Azure AI Solutions- Azure AI resource provisioning and configuration
- Responsible AI principles and governance
- Model selection and lifecycle management
Knowledge Mining and Information Retrieval- Indexing and semantic search
- Azure AI Search configuration
- RAG (Retrieval Augmented Generation) patterns
Develop Generative AI Applications and Agents- Azure OpenAI Service integration
  • 1. Function calling and tool use
    • 2. Prompt engineering and prompt flow design
      - AI agents architecture
      • 1. Agent orchestration and workflows
        • 2. Memory and state management
          Implement Computer Vision Solutions- OCR and document intelligence
          - Image classification and object detection

          Microsoft Developing AI Apps and Agents on Azure Sample Questions:

          Question #1
          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. Foundry User
          C. Foundry Account Owner
          D. Azure AI Developer


          Question #2
          You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
          You need to connect to the index to meet the following requirements:
          - Complex questions must retrieve information from multiple chunks.
          - Multi-turn conversations must influence retrieval planning.
          - Retrievals must run in parallel to reduce latency.
          Which retrieval approach should you use?

          A. classic Retrieval Augmented Generation (RAG)
          B. iterative retrieval
          C. chain of thought
          D. agentic Retrieval Augmented Generation (RAG)


          Question #3
          Hotspot Question
          You have a Microsoft Foundry project that contains an internal Q&A agent.
          Users report the following issues when they ask the agent questions:
          - An increase in the following response: "No relevant information
          found"
          - Periodic HTTP 429 rate limit exceeded errors during peak hours
          You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
          What should you do? To answer, select the appropriate options in the answer area.
          NOTE: Each correct selection is worth one point.


          Question #4
          Hotspot Question
          You have a Microsoft Foundry project that contains two agents named PolicyWriter and RskReviewer.
          PolicyWriter generates daft updates for customer polices, and RiskReviewer reviews the drafts.
          In the visual builder, you need to create a workflow that meets the following requirements:
          - Finalizes low-risk updates without manual intervention
          - Ensures predictable execution across the agents
          - Requires user approval for highs updates
          What should you configure? To answer, select the appropriate options in the answer area.
          NOTE: Each comet selection is worth one point.


          Question #5
          Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
          After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
          You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
          Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
          You need to improve response completeness.
          Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
          Does this meet the goal?

          A. No
          B. Yes


          Solutions:

          Question #1
          Correct Answer: B
          Question #2
          Correct Answer: D
          Question #3
          Correct Answer: Only visible for members
          Question #4
          Correct Answer: Only visible for members
          Question #5
          Correct Answer: A

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