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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor's logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?
A) Rerun the prompt by using a different random seed.
B) Apply a mask-based inpainting edit to the part of the image that contains the logo.
C) Increase the prompt guidance strength.
D) Modify the original prompt to exclude brand names.
2. You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?
A) Content Safety does not moderate images, only text
B) The image content model returns only severity values 0, 2, 4, and 6
C) Severity 3 is reserved for the Self-Harm category only
D) Image moderation always returns a binary blocked or allowed flag, never a severity
3. 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) monitoring
C) safety metrics
D) token usage
4. You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?
A) Call Azure AI Search directly from each application by using Microsoft Entra authentication.
B) Enable a managed identity for each application and call Azure AI Search directly.
C) Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.
D) Configure an Azure AI Search connection in Project1 and reference the connection in each application.
5. You are developing an application that will use Azure AI Search for internal documents.
You need to implement document-level filtering for Azure AI Search.
Which three actions should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A) Retrieve all the groups.
B) Add allowed groups to each index entry
C) Supply the groups as a filter for the search requests
D) Retrieve the group memberships of the user
E) Create one index per group.
F) Send access tokens from Microsoft Entra ID, with the search request.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B,C,D |
