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The Developing AI Apps and Agents on Azure (AI-103) certification exam is one of the top-rated career advancement certification exams. The Developing AI Apps and Agents on Azure (AI-103) certification exam can play a significant role in career success. With the Developing AI Apps and Agents on Azure (AI-103) certification you can gain several benefits such as validation of skills, career advancement, competitive advantage, continuing education, and global recognition of your skills and knowledge. The Developing AI Apps and Agents on Azure (AI-103) certification is a valuable credential that assists you to enhance your existing skills and experience.
| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
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NEW QUESTION # 59
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support real-time inference. The solution must meet the following requirements:
- Use key-based authentication.
- Support real-time REST API access.
- NOT consume the vCPU quota of the virtual machines in the Azure
subscription.
Which type of deployment should you use?
Answer: B
Explanation:
The most appropriate type of deployment is a serverless API deployment (also referred to as a standard deployment or Models-as-a-Service / MaaS).
Key-Based Authentication: Serverless API deployments natively provision an endpoint URL alongside primary and secondary API keys to secure your client applications.
Real-Time REST API Access: When the model is successfully deployed, it exposes a scalable, real-time HTTP/REST endpoint matching the standardized Azure AI Model Inference API.
No Virtual Machine vCPU Quota Consumption: Unlike managed compute deployments-which provision dedicated virtual machines in your subscription and require VM vCPU quota- serverless API deployments are completely hosted and managed by Microsoft. They operate on a pay-as-you-go, token-based billing structure and do not consume any VM vCPU quota from your Azure subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/deployments-overview
NEW QUESTION # 60
A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?
Answer: A
Explanation:
Prompt Shields detects prompt injection attacks, including indirect injection where malicious instructions are embedded in content the model processes, such as text hidden inside an image.
NEW QUESTION # 61
Hotspot Question
You have an Azure subscription.
You need to create a new resource that will generate fictional stores in response to user prompts.
The solution must ensure that the resource uses a customer-managed key to protect data.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: OpenAI
To generate fictional stores in response to user prompts, you need a generative AI model (such as GPT-3.5 or GPT-4). The Azure OpenAI service natively supports this generative capability and allows you to secure your deployed models and data using customer-managed keys (CMK).
Box 2: --assign-identity
When defining the Azure Key Vault resource script to enable customer-managed keys (CMK), you must use --assign-identity (or assign-identity).
Reference:
https://learn.microsoft.com/en-us/azure/search/search-how-to-managed-identities
NEW QUESTION # 62
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Answer: B
NEW QUESTION # 63
You have an Azure subscription that contains an Azure OpenAI resource.
You deploy the GPT-4 model to the resource.
You need to ensure that you can upload files that will be used as grounding data for the model.
Which two types of resources should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models.
For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
NEW QUESTION # 64
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