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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
* Multi-turn continuity within the session
* Cross-session continuity for the same case
* Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?

A) Create and reuse a conversation by storing the conversation's ID and supplying the ID on subsequent requests.
B) Enable memory summarization on the agent definition to persist the context automatically.
C) Persist only the final model response stored in the client application and prepend the response to future prompts.

2. You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool 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 have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor ' s logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?

A) Apply a mask-based inpainting edit to the part of the image that contains the logo.
B) Rerun the prompt by using a different random seed.
C) Increase the prompt guidance strength.
D) Modify the original prompt to exclude brand names.

4. You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?

A) Set the output_content_format=ContentFormat. MARKDOWN value.
B) Configure output=figures when you analyze the PDF.
C) Increase the confidence threshold.
D) Configure content=markdown when you analyze the document.

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

Solutions: