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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?

A) token usage
B) latency
C) run success rate
D) evaluation metrics

2. You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used
once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

3. You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Azure OpenAl Embedding
B) key phrase extraction
C) Text Split
D) Entity Recognition
E) Merge
F) Language Detection

4. You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
* Some answers are unsupported by retrieved documents.
* A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

5. You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?

A) Set the input_fidelity parameter to high.
B) Apply a groundedness detection filter.
C) Decrease the value of the temperature parameter.
D) Include a prompt and input image in the request.

Solutions: