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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 2: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 3: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Topic 4: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
>> AI-103 Question Explanations <<
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NEW QUESTION # 105
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?
Answer: A
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
NEW QUESTION # 106
You are building an app that will use Azure AI to monitor workspaces for safety. You need to recommend a service that meets the following requirements:
* Generates alerts when employees enter high-risk areas
* Monitors video feeds in real time
* Minimizes development effort
What should you recommend?
Answer: A
Explanation:
Azure Vision in Foundry Tools Spatial Analysis is designed to process real-time streaming video and analyze the presence, movement, and spatial relationships of people in physical environments. It provides predefined operations such as personcrossingpolygon, which can identify when a person enters or exits a configured zone. The resulting personZoneEnterExitEvent can be consumed by an application to trigger alerts when an employee enters an area designated as high risk.
Spatial Analysis minimizes development effort because it supplies pretrained people-detection and zone- monitoring capabilities. Developers configure camera streams, polygonal zones, and event parameters instead of collecting images, labeling objects, and training a custom model.
Image Analysis primarily evaluates individual images and does not maintain movement or zone state across a live stream. Azure AI Video Indexer extracts searchable insights from video or live-stream content, but it is not the specialized option for detecting physical zone-entry events. Custom Vision object detection would require training data, labeling, model training, deployment, and additional application logic to track movement.
The Study Guide explicitly associates Spatial Analysis with detecting the presence and movement of people in video under Implement computer vision solutions # Analyze videos .
NEW QUESTION # 107
You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search.
Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations.
You are provided with the following code snippet that runs the agent.
You need to add the correct tool _choiceparameter to the code to deterministically force the agent to invoke the MCP tool on each run.
What should you add?
Answer: C
Explanation:
To deterministically force the agent to invoke your Model Context Protocol (MCP) tool on every run, you must pass tool_choice="required" into the run_create_and_process method.
The 'required' tool choice: Setting this parameter to 'required' forces the underlying Azure OpenAI model to invoke one of your available tools on every response, ensuring the agent doesn't guess answers from the base model.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-best-practice
NEW QUESTION # 108
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?
Answer: C
Explanation:
To meet your security and architecture requirements, you must add the Azure AI Search instance as a Connection within your Azure AI Foundry project and configure Managed Identities for role- based access control (RBAC).
To securely unify your search configuration without API keys, add the Azure AI Search instance as a shared Connection in your Azure AI Foundry project, disable key authentication on the search service, and authorize your applications using Azure RBAC and Managed Identities.
Note:
*-> 1. Create a Project Connection
Connect Azure AI Search directly inside the Azure AI Foundry hub or project.
*-> Share the same search service configuration across all connected client applications automatically.
Centralize your search endpoint details to reduce administrative overhead.
2. Disable Key Authentication
3. Enable Managed Identities
4. Update the Web App Code
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/tutorials/copilot-sdk-create-resources
NEW QUESTION # 109
You have a Microsoft Foundry resource named Al1 that hosts three deployments of the GPT 3.5 model. Each deployment is optimized for a unique workload.
You plan to deploy three apps. Each app will access AM by using the REST API and will use the deployment that was optimized for the apps intended workload.
You need to provide each app with access to All and the appropriate deployment. The solution must ensure that only the apps can access AH.
What should you use to provide access to AI1. and what should each app use to connect to its appropriate deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use Microsoft Entra ID authentication and pass an OAuth 2.0 bearer token in the REST request's Authorization header. Assign the required inference role, such as Cognitive Services User , only to each application's managed identity or service principal. This authenticates the applications as distinct security principals and supports resource-scoped RBAC, auditing, token expiration, and immediate access revocation.
Microsoft recommends Entra ID for production workloads because API keys are static, resource-wide secrets that cannot identify individual callers or provide granular authorization.
Each application must specify its appropriate deployment name when making an inference request. Foundry deployments assign a unique name and configuration to each deployed model. In the deployment-level REST API, that name appears in the request route:
/openai/deployments/{deployment-name}/chat/completions
The resource endpoint and authentication mechanism can remain common across all three applications, while the deployment name directs each request to the GPT-3.5 deployment optimized for that application's workload. A deployment type describes capacity or provisioning characteristics and does not select a particular deployed model.
Study Guide alignment: configure authentication, managed identities, RBAC, model deployments, and REST-based model consumption.
NEW QUESTION # 110
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