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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an Azure AI agent solution.
You plan to create an agent-based app named App1 that will analyze and summarize data for users and generate data-driven recommendations. App1 will be used by non-technical business users and must adapt to new and unforeseen business challenges and improve its performance over time.
You need to identity which type of agent to use in App1. The solution must meet the following requirements:
- Adapt and improve the agents' performance over time based on user
feedback.
- Provide tailored recommendations to help users make informed
decisions.
- Provide the best possible performance of the app.
Which agent type should you identify?

A) cognitive
B) task automation
C) autonomous
D) prompt-and-response

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

3. You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
- Provide accurate, context-aware responses grounded in internal
product documentation stored in Azure AI Search.
- Require deep, multi-step reasoning across long contexts.
- Generate detailed natural language responses.
Which type of model should you use to power the agent?

A) a large language model (LLM)
B) a key phrase extraction model
C) a multimodal model
D) a small language model (SLM)

4. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for documents.
Does this meet the goal?

A) Yes
B) No

5. 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) token usage
D) safety metrics

Solutions: