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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.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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

3. You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search.
Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations.
You are provided with the following code snippet that runs the agent.
run = project_client.agents.runs.create_and_process(
thread_id=thread.id,
agent_id=agent.id,
)
You need to add the correct tool_choice parameter to the code to deterministically force the agent to invoke the MCP tool on each run.
What should you add?

A) tool_choice={ " type " : " knowledge_base " }
B) tool_choice={ " auto " }
C) tool_choice={ " required " }
D) tool_choice ={ " type " : " mcp " }

4. You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
* Ensure that background objects can be removed by applying a mask-based inpainting edit.
* Preserve the original lighting and style of the edited images.
* Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?

A) Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify.
B) Enable image_to_image mode and a high-strength value to regenerate the full image based on the prompt.
C) Set generation mode to image_variation and provide the original image as a reference.
D) Enable text_to_image mode and a prompt describing the desired background removal.

5. You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
. Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.
. Operate with low latency to maintain a conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?

A) Use an embeddings model to encode the audio, and then decode the audio into text and speech.
B) Use real-time speech to text for incoming audio and text to speech for agent responses.
C) Use batch transcription to convert the audio input and return text responses from the agent.
D) Use speech translation to convert the audio into another language and return the translated text.

Solutions: