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| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 2: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Topic 3: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
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21. Frage
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
22. Frage
You have an Azure subscription that contains an Azure AI Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure AI Content Understanding project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?
Antwort: B
Begründung:
Cognitive Services Contributor
This role is typically granted access at the resource group level for a user in conjunction with additional roles. By itself this role would allow a user to perform the following tasks.
* Create new Azure OpenAI resources within the assigned resource group.
* Etc.
To create an Azure AI Content Understanding project on an Azure AI Foundry hub, you need either the Azure AI Account Owner, Contributor, or Cognitive Services Contributor role at the subscription level to create the necessary Azure AI Foundry resource, and then the user must have the Azure AI User role at the project level to create the agent within that project.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control
23. Frage
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?
Antwort: B
Begründung:
You should enable mask_inpainting and supply both the input image and the mask to meet all the criteria for your Microsoft Foundry image-editing workflow.
By utilizing Microsoft Foundry's built-in Image Generation Tool parameters, configuring the workflow this way ensures the desired edits are perfectly executed.
Workflow Configuration Requirements
Mask-Based Object Removal: Passing the mask parameter explicitly flags the exact background object regions targeted for removal, replacing them seamlessly.
Preserving Style and Lighting: Enabling mask_inpainting prompts the underlying built-in model (such as gpt-image-2) to inherit and maintain the exact lighting, textures, and style of the surrounding unmasked environment.
Built-In Controls Only: This is entirely handled natively via the Foundry Agent Service API variables without deploying a single line of custom code or third-party model checkpoints.
Targeted Area Enforcement: The system relies on the provided mask array to ensure pixels outside the marked coordinates are kept completely untouched and protected from VAE degradation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/image-generation
24. Frage
You are building an app that will share user images. You need to configure the app to:
* Categorize each image as a photograph or drawing.
* Generate a caption for the image.
* Minimize development effort.
Which two services should you include?
Antwort: A,B
Begründung:
Use image type detection to distinguish drawings from ordinary photographic content. Azure Vision's ImageType visual feature returns information including lineDrawingType, which indicates whether the submitted image is a line drawing, and clipArtType, which estimates whether it is clip art. The application can use these pretrained results to implement the required photograph-versus-drawing categorization without collecting labeled images or training a custom classifier.
Use image descriptions to generate the caption. Azure Vision Image Analysis provides the Caption visual feature, which generates a concise, one-sentence description of the overall image. Dense Captions can additionally describe individual regions, but the standard Caption feature directly satisfies the stated requirement.
Object detection identifies and locates individual objects by using bounding boxes; it does not determine whether the entire image is a photograph or drawing. Content tags produce descriptive keywords rather than a natural-language caption. Azure Custom Vision classification could be trained for the image-type distinction, but it would require image collection, labeling, training, evaluation, and deployment, increasing development effort unnecessarily.
Study Guide alignment: configure applications to produce captions and implement solutions that identify visual characteristics within images .
25. Frage
You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
* Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search.
* Require deep, multi-step reasoning across long contexts.
* Generate detailed natural language responses.
Which type of model should you use to power the agent?
Antwort: B
Begründung:
The correct model type is a large language model (LLM) . The scenario requires an agent that can reason over retrieved documentation, synthesize context-aware answers, and generate detailed natural language responses. Microsoft Foundry RAG guidance defines Retrieval Augmented Generation as a pattern that combines search with large language models so responses are grounded in organizational data, which directly matches the use of Azure AI Search for internal product documentation.
An LLM is also the correct fit for deep, multi-step reasoning across long contexts. Azure AI Search agentic retrieval guidance states that complex chat and agent scenarios use an LLM to break a user query into smaller focused subqueries for better coverage over indexed content. Microsoft also describes Azure AI Search as a way to ground agents and chatbots in proprietary enterprise data for accurate, context-aware responses.
A multimodal model is unnecessary because the requirements are text-focused, not image, audio, or video based. A key phrase extraction model performs narrow text analytics and cannot generate detailed answers.
An SLM may reduce cost and latency, but the stated need for deep reasoning and long-context synthesis favors an LLM. Reference topics: Microsoft Foundry RAG, Azure AI Search grounding, agentic retrieval, and model selection for generative AI agents.
26. Frage
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