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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. 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) monitoring
B) tracing
C) safety metrics
D) token usage

2. 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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the max_tokens parameter.
Does this meet the goal?

A) Yes
B) No

3. You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers.
The results are often
incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?

A) an Azure Machine Learning model
B) Azure Language in Foundry Tools
C) Azure Content Understanding in Foundry Tools

4. You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. You have a Microsoft Foundry project that uses Azure Al Search to ground an agent in internal documentation.
After a recent content update, users report that the agent ' s answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model ' s generated responses.
Which observability signal should you review?

A) indexer status and failure history
B) groundedness evaluation metrics
C) prediction drift metrics
D) latency breakdown traces

Solutions: