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Microsoft AI-103 : Developing AI Apps and Agents on Azure
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
- Updated: Sep 17, 2026 Q&As: 159 Questions and Answers
About Microsoft AI-103 Exam
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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 2: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 3: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 4: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 5: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Question #1
In mid-2026 you are starting a brand-new agentic application on Azure and want to build on the generally available, supported entry point for Foundry agents. Which API should you target?
- A. The LUIS runtime API
- B. The Assistants API
- C. The Responses API via Foundry Agent Service
- D. The legacy text Completions API
Correct Answer: C 🗳️
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Question #2
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
- A. Whisper
- B. embeddings
- C. GPT-4
- D. DALL-E
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
Question #3
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:
Question #4
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
Question #5
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 Extraction
- C. Document Layout
- D. GenAI Prompt
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
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