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Updated: Jul 28, 2026
No. of Questions: 159 Questions & Answers with Testing Engine
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Microsoft AI-103 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Developing AI Apps and Agents on Azure AI |
| Exam Number: | AI-103 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700/1000 |
| Exam Format: | Multiple choice, Multiple response, Case studies, Drag and drop, Scenario-based questions |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure AI Fundamentals Microsoft Certified: Azure Developer Associate |
| Exam Duration: | 120-180 |
| Available Languages: | English |
| Exam Price: | USD 165 (may vary by region) |
| Certificate Validity Period: | 1 year (renewable via Microsoft Learn assessment) |
| Recommended Training: | Microsoft Learn - Azure AI Engineer Learning Path Azure OpenAI Service Documentation |
| Exam Registration: | Pearson VUE Microsoft Exams Microsoft Certification Dashboard |
| Sample Questions: | Microsoft AI-103 Sample Questions |
| Exam Way: | Online proctored exam or authorized test center |
| Pre Condition: | Recommended: experience with Azure services, Python or C#, and basic machine learning concepts. Prior knowledge of Azure AI-102 or equivalent is helpful but not mandatory. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/ |
Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. 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.
2. You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
A) Add Agent1 to the Microsoft Foundry playground.
B) Provision an Application Insights resource.
C) Create a Microsoft Foundry project.
D) Add Agent1to a Microsoft Foundry project.
3. Hotspot Question
You have a Python application that redacts sensitive information before sending prompt text to a language model. The application has the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
4. Drag and Drop Question
You have a Microsoft Foundry project that contains an agent. The agent uses threads and file uploads and calls an Azure OpenAI model deployment.
During load testing, calls intermittently fall and return an HTTP 429 rate limit exceeded error.
Some user uploads fail and generate an HTTP 400 file size exceeded error.
You need to mitigate the errors and reduce call failures. The solution must remain within the service and model limits.
What should you do to resolve each error? To answer, drag the appropriate actions to the correct errors. Each action may be used once, more than once or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each comet selection is worth one point.
5. Hotspot Question
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
- Use a single key and endpoint to access multiple services.
- Consolidate billing for future services that you might use.
- Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: C | Question # 3 Answer: Only visible for members | Question # 4 Answer: Only visible for members | Question # 5 Answer: Only visible for members |
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