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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?

A) Implement a retry policy that uses exponential backoff and jitter.
B) Reduce the number of registered tools.
C) Spit uploaded content into smaller files.
D) Create a new thread and retry the calls immediately.

2. You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?

A) Add Agent1to a Microsoft Foundry project.
B) Add Agent1 to the Microsoft Foundry playground.
C) Provision an Application Insights resource.
D) Create a Microsoft Foundry project.

3. A production application authenticates to Microsoft Foundry using an API key stored in an environment variable. Your security team requires that you remove hardcoded secrets and enable per-principal auditing of every call. Which approach meets both requirements?

A) Authenticate with Microsoft Entra ID using a managed identity (keyless)
B) Embed the API key in the deployment pipeline as a masked variable
C) Move the API key into Azure Key Vault and rotate it monthly
D) Share a single API key across services and restrict it with IP allow-listing

4. A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?

A) Classic keyword search with a scoring profile
B) Agentic retrieval
C) A single vector query with a high similarity threshold
D) Increasing the top-k value on a single keyword query

5. You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?

A) an Azure Language in Foundry Tools text analysis model deployment
B) an Azure Content Understanding in Foundry Tools analyzer
C) a generative chat completion request
D) an Azure OpenAI Responses API call that uses a multimodal model

Solutions: