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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

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

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

3. 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) the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
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) 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

4. You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding locations within the source document.
What should you do?

A) Enable estimateFieldSourceAndConfidence.
B) Set enableSegment to true.
C) Provide labeled samples.
D) Configure the analyzer to use generative extraction for all fields.

5. You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?

A) Create a new thread and retry the calls immediately.
B) Reduce the number of registered tools.
C) Split uploaded content into smaller files.
D) Implement a retry policy that uses exponential backoff and jitter.

Solutions: