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| Section | Objectives |
|---|---|
| Topic 1: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 4: Connect to and consume Azure services | - Integrate Azure services
|
DumpTOP의Microsoft인증 AI-200덤프는 인터넷에서 검색되는Microsoft인증 AI-200시험공부자료중 가장 출중한 시험준비 자료입니다. Microsoft인증 AI-200덤프를 공부하면 시험패스는 물론이고 IT지식을 더 많이 쌓을수 있어 일거량득입니다.자격증을 취득하여 자신있게 승진하여 연봉협상하세요.
질문 # 72
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features 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:
* Support percentage-based, targeted rollouts: Feature flag
* Maximize token limits per request: Key-value
* Store a key that is retrieved securely at runtime: Key Vault reference
* Reference a list of supported deployment regions: Key-value
Azure App Configuration provides three distinct mechanisms for these scenarios. A Feature flag is the correct choice for percentage-based and targeted rollouts. Feature Management supports conditional activation based on filters such as percentage allocation, users, groups, and other targeting criteria, making it appropriate for gradual deployment and controlled experimentation.
A Key-value is the correct mechanism for ordinary runtime configuration data such as a model token limit or a list of supported deployment regions. These values are application settings rather than secrets or feature- state controls. The application can retrieve and refresh them dynamically without redeployment. A token-limit setting such as MaxTokensPerRequest and a region list such as SupportedRegions fit naturally into App Configuration key-value entries.
For sensitive data, use a Key Vault reference . App Configuration stores only the reference to the Azure Key Vault secret rather than the secret value itself. At runtime, the application resolves the secret securely, typically by using a managed identity. This avoids embedding credentials or sensitive keys in configuration values.
Therefore, the correct mapping is Feature flag # Key-value # Key Vault reference # Key-value .
Study Guide references: Azure App Configuration # Feature Management; key-values; Key Vault references; dynamic application configuration.
질문 # 73
You must ensure an Azure OpenAI-powered application never exceeds a defined token-per-minute budget across all users to avoid runaway costs.
What should you configure?
정답：A
설명：
Configure deployment-level Tokens-per-Minute (TPM) and Requests-per-Minute (RPM) rate limits for the Azure OpenAI model deployment. Azure OpenAI quota is allocated to individual model deployments in TPM units, and Microsoft documents that the TPM assigned to a deployment directly maps to the rate limit enforced for inference requests against that deployment. An associated RPM limit is also enforced according to the model ' s TPM-to-RPM ratio.
Because all application users invoke the same deployment, this enforcement occurs at the deployment boundary rather than independently for each end user. Once request traffic would exceed the configured capacity, Azure applies throttling rather than allowing unrestricted token consumption. Microsoft Foundry additionally supports explicit token-management controls through AI Gateway, where a TPM limit can be set for a model deployment; requests exceeding the limit receive HTTP 429 Too Many Requests responses.
Content filtering governs safety categories, not consumption. An Azure AI Search vector-index limit controls search storage rather than generated tokens. Temperature changes sampling randomness and has no enforcement role in rate or cost control.
Study Guide references: Azure OpenAI quota management; TPM/RPM rate limits; deployment capacity; throttling; Foundry AI Gateway token management.
질문 # 74
Drag and Drop Question
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
정답：
설명：
Explanation:
Step 1: Enable ingress
You must first expose your container app to external traffic to generate the default fully qualified domain name (FQDN) needed for DNS mapping.
Step 2: Add DNS records to the domain provider
Log into your domain registrar to create the required TXT (for verification) and CNAME/A records pointing to your container app.
Step 3: Validate the custom domain name
Azure checks your DNS records to confirm that you actually own the domain before allowing it to be linked.
Step 4: Add the custom domain name
Once validation passes, you officially add and register the custom domain name within the Azure Container App configuration.
Step 5: Bind the certificate
Finally, bind an SSL/TLS certificate to the custom domain to secure the connection and enable HTTPS access.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/ingress-overview
질문 # 75
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
정답：B,C
설명：
Detailed Explanation: Azure Container Apps uses KEDA-based scale rules for event-driven workloads. A Service Bus scaler can monitor queue depth and create replicas only when messages are pending. To meet the explicit cost requirement, the scale configuration must permit a minimum replica count of zero so all active replicas can terminate when the queue is empty. Increasing the maximum replica count changes peak capacity, and HTTP ingress concurrency is not the correct scaling signal for a background Service Bus consumer.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Set scaling rules in Azure Container Apps
질문 # 76
You need to deploy Azure Function resources and apps to meet the business and technical requirements.
What should you use?
정답：B
설명：
Use GitHub Actions because Proseware requires an automated, version-controlled deployment pipeline and explicitly prohibits local and command-line deployments. Microsoft documents GitHub Actions as a supported CI/CD mechanism for Azure Functions: a workflow stored under .github/workflows can build and deploy Function app code automatically in response to repository events such as pushes or pull requests. The Azure Functions deployment action can deploy the application package directly to the target Function app.
GitHub Actions also satisfies the infrastructure requirement. Microsoft provides first-class support for deploying Bicep templates through GitHub Actions , enabling Azure resources to be provisioned declaratively from the same source-controlled pipeline. A pull-request workflow can perform validation or Bicep what-if analysis before changes are merged, while a main-branch workflow can perform the actual deployment after approval. This provides repeatability, auditing, and separation from individual developer machines.
Azure Functions Core Tools and Azure CLI are commonly used for interactive/local deployments, which directly conflicts with the requirement to eliminate command-line deployment. Local Git deployment also fails the requirement for a centrally automated Bicep-based deployment process.
Study Guide references: Azure Functions CI/CD; GitHub Actions; infrastructure as code with Bicep; automated Azure deployments; version-controlled deployment workflows.
Category Breakdown
Category Number of Questions
Connect to and consume Azure services 41
Develop AI solutions by using Azure data management services 39
Secure, monitor, troubleshoot Azure solutions 34
Develop containerized solutions on Azure 28
TOTAL 142
Exam Topic Breakdown
Exam Topic Number of Questions
질문 # 77
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DumpTOP에는Microsoft AI-200인증시험의 특별한 합습가이드가 있습니다. 여러분은 많은 시간과 돈을 들이지 않으셔도 많은 IT관련지식을 배우실수 있습니다.그리고 빠른 시일 내에 여러분의 IT지식을 인증 받으실 있습니다. DumpTOP인증자료들은 우리의 전문가들이 자기만의 지식과 몇 년간의 경험으로 준비중인 분들을 위하여 만들었습니다.
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