Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. 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 basic parsing and fixed-size 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 OCR and page-level chunking.

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 run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?

A) No
B) Yes

3. 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 user prompts.
Does this meet the goal?

A) No
B) Yes

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 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 text_to_image mode and a prompt describing the desired background removal.
C) Set generation mode to image_variation and provide the original image as a reference.
D) Enable image_to_image mode and a high-strength value to regenerate the full image based on the prompt.

Solutions: