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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts. What should you implement?

A) Increase temperature to encourage exploration
B) Use a larger context window model with no retrieval
C) Disable content filtering to allow more flexible answers
D) Connect the agent to an Azure AI Search knowledge source and instruct it to answer only from retrieved content

2. You are developing an Azure Function that calls external APIs by providing an access token for the API. The access token is stored in a secret named token in an Azure Key Vault named mykeyvault. You need to ensure the Azure Function can access the token. Which value should you store in the Azure Functions app configuration?

A) App:Settings:Secret:mykeyvault:token
B) @Microsoft.KeyVault(SecretUri=https://mykeyvault.vault.azure.net/secrets/token/)
C) KeyVault:mykeyvault;Secret:token
D) AZUREKVCONNSTR_ https://mykeyvault.vault.azure.net/secrets/token/

3. 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.

4. You must ensure that PII (personally identifiable information) such as names and phone numbers is automatically redacted from support ticket transcripts before they are stored. What should you use?

A) Azure AI Content Safety
B) Azure AI Search semantic configuration
C) Azure Key Vault
D) Azure AI Language PII detection and redaction

5. Your application stores conversation history so a multi-turn chatbot can reference earlier turns, but the context window is limited. You need to keep the most relevant history within token limits.
What should you implement?

A) Truncate all history after 2 turns
B) Increase max_tokens for output only
C) Store history only in the client and never send it to the model
D) Summarize older turns and retain recent turns verbatim

Solutions: