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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 3: Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Topic 4: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
1. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?
A) Yes
B) No
2. You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?
A) Azure AI Content Safety
B) Cross-Origin Resource Sharing (CORS) rules
C) API key rotation
D) A private endpoint with Azure Private Link
3. A RAG application's retrieval step is returning documents that are topically related but not precisely relevant to the user's question. You need to improve retrieval precision without retraining any model. What should you do?
A) Disable vector search and use only keyword search
B) Increase the embedding model's dimensionality only
C) Reduce the number of indexed documents
D) Enable semantic ranking (re-ranking) in Azure AI Search
4. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Embed the API key as a hardcoded environment variable in the Dockerfile.
Does the solution meet the goal?
A) Yes
B) No
5. Hotspot Question
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: Only visible for members |



