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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. Hotspot Question
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
- Retain user preferences across multiple conversations.
- Enable users to provide contextual grounding by directly uploading
documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. You have an Azure subscription that contains an Azure Al Foundry instance named AI1.
You have an app that automatically triages and resolves issues presented in the log files of a system.
You create an incident manager agent and a DevOps agent that collaborate to resolve the issues.
You need to ensure that the incident manager agent can assign work to the DevOps agent. The solution must minimize development effort.
What should you do?

A) Configure prompt flow for AI1.
B) Create a plugin for the DevOps agent.
C) Configure a connected agent for AI1.
D) Create a plugin for the incident manager agent.

3. Hotspot Question
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Identify each presenter in the video and attribute each text file to
sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?

A) abstractive summarization
B) sentiment analysis
C) key phrase extraction
D) extractive summarization

5. You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?

A) Use OCR and page-level chunking.
B) Use advanced data parsing to reingest the documents.
C) Use page-level OCR extraction and store each page as a single chunk.
D) Use basic parsing and fixed-size chunking.

Solutions: