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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?

A) an Azure OpenAl Responses API call that uses a multimodal model
B) a generative chat completion request
C) an Azure Content Understanding in Foundry Tools analyzer
D) an Azure Language in Foundry Tools text analysis model deployment

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

3. You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.
You need to implement two pipelines by using Azure Content Understanding in Foundry Tools. The solution must meet the following requirements:
* Include a pipeline named Pipeline1 that supports cost-effective, high-volume processing of standalone PDF invoices.
* Include a pipeline named Pipeline2 that supports cross-document validation by using multi-step reasoning and reference data.
How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration 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.

4. 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.

5. 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 Al 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) No
B) Yes

Solutions: