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| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
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NEW QUESTION # 99
Drag and Drop Question
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 100
You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?
Answer: A
NEW QUESTION # 101
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area
. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
NEW QUESTION # 102
Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 103
An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
Answer: B
Explanation:
Azure Private Endpoints are network interfaces that connect your Azure ML workspace to your Virtual Network using a private IP address from the VNet ' s address space. Once a private endpoint is created and DNS is configured, all traffic to the workspace - including the Studio UI, REST API, and SDK calls - travels entirely over Microsoft ' s private backbone rather than the public internet. The workspace ' s public endpoint can then be completely disabled. Azure Firewall (option A) filters traffic at the network layer but still requires the workspace to have a public IP. Network Security Groups (option C) control traffic within VNets but cannot block the public endpoint of a PaaS service. Service endpoints (option D) keep traffic on the Azure backbone but the workspace still has a public-facing address. Private endpoints are the only option that fully removes the public network presence.
Microsoft Learn Reference Topic: Configure a private endpoint for Azure Machine Learning workspace
NEW QUESTION # 104
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