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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?

A) Enable prompt shields.
B) Apply keyword scanning to optical character recognition (OCR) output by using Azure Vision in Foundry Tools.
C) Implement image moderation.
D) Use blocklists.

2. You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?

A) Enable response streaming.
B) Switch to Retrieval Augmented Generation (RAG).
C) Add a reflection pass before the responses are returned.
D) Decrease the value of the temperature parameter.

3. 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) Enable text_to_image mode and a prompt describing the desired background removal.
D) Set generation mode to image_variation and provide the original image as a reference.

4. You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
* Extracts the invoice number, invoice date, vendor name, and total amount across varying templates
* Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?

A) a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
B) the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
C) a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
D) the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer

5. You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
* An if/else condition expression that ensures that Var01 contains a value
* A Send message expression that returns the stored user response in uppercase How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: