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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 2: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 3: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
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NEW QUESTION # 43
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
Answer: D
NEW QUESTION # 44
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
Answer: B
Explanation:
To monitor and control access to a custom agent in Azure, you must first create a Microsoft Foundry project. Once the project is created, you register your custom agent within it to enable management capabilities such as access control and activity monitoring.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/control-plane/register-custom-agent
NEW QUESTION # 45
You have an Azure AI agent solution.
You plan to create an agent-based app named App1 that will analyze and summarize data for users and generate data-driven recommendations. App1 will be used by non-technical business users and must adapt to new and unforeseen business challenges and improve its performance over time.
You need to identity which type of agent to use in App1. The solution must meet the following requirements:
- Adapt and improve the agents' performance over time based on user
feedback.
- Provide tailored recommendations to help users make informed
decisions.
- Provide the best possible performance of the app.
Which agent type should you identify?
Answer: A
Explanation:
A cognitive agent is designed to mimic human thought processes, making it ideal for the complex, data-driven tasks described in the query.
Analyze and summarize data and generate data-driven recommendations:
Cognitive agents use machine learning and natural language processing to analyze historical data and act on their learnings, which includes generating insights and recommendations.
Adapt and improve over time based on user feedback:
They are designed to learn and adapt to user behavior and preferences over time, improving their responses and performance through feedback.
Used by non-technical business users:
These agents can be accessed through user-friendly interfaces, often natural language-based (like virtual assistants), making them suitable for non-technical users.
Provide tailored recommendations to help users make informed decisions:
By learning from data and interactions, they can deliver personalized recommendations that support better decision-making.
NEW QUESTION # 46
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?
Answer: C
Explanation:
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
NEW QUESTION # 47
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Answer: D
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 48
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