Microsoft AI-200인기자격증덤프공부자료 & AI-200최고품질인증시험덤프데모

Microsoft인증 AI-200시험은 멋진 IT전문가로 거듭나는 길에서 반드시 넘어야할 높은 산입니다. Microsoft인증 AI-200시험문제패스가 어렵다한들DumpTOP덤프만 있으면 패스도 간단한 일로 변경됩니다. DumpTOP의Microsoft인증 AI-200덤프는 100%시험패스율을 보장합니다. Microsoft인증 AI-200시험문제가 업데이트되면Microsoft인증 AI-200덤프도 바로 업데이트하여 무료 업데이트서비스를 제공해드리기에 덤프유효기간을 연장해는것으로 됩니다.

Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement and monitor AI workloads- Monitor performance and troubleshoot issues
- Deploy AI models and services
Topic 2: Plan and manage Azure AI solutions- Plan security and compliance requirements
- Select appropriate Azure AI services
- Monitor and optimize AI solutions
Topic 3: Implement Azure AI solutions- Implement generative AI solutions using Azure OpenAI
- Implement natural language processing solutions
- Implement computer vision solutions
- Implement knowledge mining with Azure AI Search

>> Microsoft AI-200인기자격증 덤프공부자료 <<

AI-200최고품질 인증시험덤프데모 - AI-200완벽한 시험기출자료

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최신 Azure AI Engineer Associate AI-200 무료샘플문제 (Q48-Q53):

질문 # 48
Hotspot Question
You are developing several microservices to run on Azure Container Apps. External HTTP ingress traffic has been enabled for the microservices.
A deployed microservice must be updated to allow users to test new features. You have the following requirements:
- Enable and maintain a single URL for the updated microservice to
provide to test users.
- Update the microservice that corresponds to the current microservice
version.
You need to configure Azure Container Apps.
Which features should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답:

설명:

Explanation:
Box 1: Revision label
You should use the revision labels feature in Azure Container Apps. This feature allows you to assign a dedicated, permanent URL to a specific version (or revision) of your microservice.
Box 2: Revision mode
The application's Revision mode must be configured to Multiple. This allows the existing (current) microservice version to remain active and serve production traffic concurrently while you spin up the updated revision for the test users to access safely.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/microservices


질문 # 49
You are configuring sampling for a distributed application that sends traces to Azure Monitor. The solution must:
* Preserve upstream sampling decisions across distributed traces
* Capture all spans during local testing.
* Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once or not at all. You may need to move the split bar Between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

정답:

설명:

Explanation:

Verified Answer: Preserve parent sampling decisions: ParentBasedSampler. Capture all spans locally:
AlwaysOnSampler. Sample 10% in production: TraceIdRatioBasedSampler(0.1).
Detailed Explanation: Parent-based sampling propagates the sampling decision from the upstream parent, preventing broken sampling decisions across a distributed trace. AlwaysOnSampler records every span, which is suitable for local test environments where complete trace visibility is more important than telemetry volume. TraceIdRatioBasedSampler with 0.1 selects approximately ten percent of traces based on trace identifiers, providing deterministic fixed-ratio sampling for production. A batch span processor and an exporter control processing/export, not the sampling decision itself.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | OpenTelemetry sampling in Azure Monitor | Enable Azure Monitor OpenTelemetry


질문 # 50
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?

정답:A


질문 # 51
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic refresh.
The solution must support dynamic configuration refresh while ensuring that secrets are NOT stored in App Configuration.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

정답:A,D,E

설명:
[A]
Setting up a cache expiration or polling interval enables the application to detect configuration changes periodically without restarting, fulfilling the requirement for dynamic refresh.
[B]
Using Azure Managed Identities eliminates hardcoded credentials. It allows your application to securely authenticate to both Azure App Configuration and Azure Key Vault.
[C]
Moving API keys to Azure Key Vault adheres to security best practices. Azure App Configuration can reference these secrets securely using Key Vault references, ensuring secrets are never stored as plaintext in the configuration store.
Reference:
https://smartbridge.com/azure-ai-foundry-enterprise-guide-2/


질문 # 52
You are building a multi-agent solution in Azure AI Foundry where one agent handles scheduling and another handles billing questions, and a request may need both. What should you implement?

정답:A

설명:
Multi-agent orchestration patterns use a coordinating (orchestrator) agent to decompose a request, delegate sub-tasks to specialized agents, and aggregate their outputs -- this is more maintainable and accurate than one broad prompt or model trying to cover disparate domains.


질문 # 53
......

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