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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. Hotspot Question
You are using Python SDK to develop a containerized AI application that retrieves the value of a runtime setting stored in an Azure App Configuration resource. The application will run in Azure in the security context of a managed identity.
The application must work in a local development environment without any code changes.
You need to complete the code that implements the retrieval.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. You are evaluating a fine-tuned Azure OpenAI model against the base model before promoting it to production. Which Azure AI Foundry capability should you use?

A) Prompt flow bulk testing / evaluation with metrics (groundedness, coherence, relevance)
B) Azure AI Search indexer scheduling
C) Azure Monitor Application Insights only
D) Content Safety category configuration

3. You are choosing an embedding strategy for a RAG solution. Documents range from 2 to 200 pages. You need to preserve semantic coherence while staying within embedding model token limits. What should you do?

A) Embed each entire document as a single vector
B) Chunk documents into smaller overlapping segments before embedding
C) Convert documents to images and use image embeddings
D) Use only document titles for embedding

4. You deploy a production Azure Function app that connects to an Azure SQL Database.
The solution must provide the following functionality:
- Prevent secrets from being exposed in source control.
- Support secret rotation without redeploying the function app.
- Avoid downtime during credential updates.
You need to configure secure and maintainable secret management.
What should you configure?

A) Application settings with Key Vault references
B) Hard-coded connection string in the startup class
C) Parameter file stored in source control
D) Environment variables in local.settings.json

5. You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification. What should you implement?

A) Use only the base model with no retrieval
B) Increase top_p to broaden token sampling
C) Enable higher content filter severity
D) Configure the RAG pipeline to return retrieved document metadata (source, page) alongside generated answers and instruct the model to cite them

Solutions: