Microsoft Developing AI Apps and Agents on Azure : AI-103

You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?

• A. vector search only
• B. keyword search only
• C. hybrid search
• D. semantic search only

Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).

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

Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).

You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
- Complex questions must retrieve information from multiple chunks.
- Multi-turn conversations must influence retrieval planning.
- Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?

• A. iterative retrieval
• B. classic Retrieval Augmented Generation (RAG)
• C. agentic Retrieval Augmented Generation (RAG)
• D. chain of thought

Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).

Hotspot Question
You have a Microsoft Foundry project that contains a customer support application.
You create an evaluation named Run1 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a medium severity threshold You create an evaluation named Run2 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a high severity threshold
You run both evaluations against a dataset named DB1 and receive the following results:
- Content harm defect rate of Run1: 12%
- Content harm defect rate of Run2: 4%
- Protected material evaluation of Run1: 6%
- Protected material evaluation of Run1: 6%
You start a fine-tuning job by using DB1. The job fails during automatic RAI checks for multiple content harm types.
You discover that the content filtering configuration is set to high severity.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: No
Changing the content filtering configuration to low severity will not resolve the fine-tuning job issues.
Box 2: Yes
The difference between a 12% defect rate in Run1 and a 4% defect rate in Run2 is entirely consistent with the different severity thresholds configured for the two evaluations.
Box 3: Yes
The protected material evaluation metric is entirely unaffected by changes to the content harm severity threshold.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-evaluation/azure.ai.evaluation

You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?

• A. Enable logging by using the client SDK for Content Safety.
• B. Route requests through the Azure OpenAI endpoint.
• C. Enable logging by using Foundry Local.
• D. Enable application tracing in Project1.

Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).