Microsoft 是一個成功的公司，提供各種認證和考試。通過 AI-200 考試是其中的核心要求。將帶來一個新的前沿，對你的職業道路起著如此重要的角色。AI-200 認證考試的考題按照相同的教學大綱，其次是實際的 Microsoft 的 AI-200 認證考試，我們也是不斷的升級我們的培訓資料，你得到的所有產品高達1年的免費更新，你也可以隨時延長更新訂閱時間，你將得到更多的時間來充分準備考試。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 3: Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Topic 4: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
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問題 #84
You are developing several microservices to run on Azure Container Apps.
You need to monitor and diagnose the microservices.
Which features should you use? To answer, select the appropriate feature in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
View console logs from a container in near real-time: Log streaming
* Debug the microservice from inside the container: Container console
For near-real-time troubleshooting, use Log streaming . Azure Container Apps supports streaming both system logs and container console logs directly through the Azure portal or Azure CLI. Microsoft documents that container console logs originate from the application ' s stdout and stderr streams, and the log-streaming feature can display them in near real time. This is the correct feature when developers need immediate visibility into container output without waiting for log ingestion into a monitoring workspace.
To debug the application from inside the running container , use the Container console . Azure Container Apps allows operators to connect interactively to a container by using the portal console or the az containerapp exec command. From there, they can run commands through shells such as bash or sh, inspect files, test network connectivity, examine environment variables, and troubleshoot runtime behavior directly in the container context.
Azure Monitor metrics are appropriate for aggregated performance measurements rather than interactive container debugging. Azure Monitor Log Analytics is useful for historical queries and centralized analysis, but Microsoft notes that ingested logs can have a delay of several minutes, so it is not the best choice for near- real-time console output.
Study Guide references: Azure Container Apps # Log streaming; Container console; observability; Azure Monitor integration; interactive troubleshooting.
問題 #85
Hotspot Question
You deploy a private container image from Azure Container Registry (ACR) to App Service.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: Assign the Container Registry Reader role to a managed identity.
To securely deploy a private container image from Azure Container Registry (ACR) to an Azure App Service without managing passwords, you should configure a Managed Identity on the App Service and grant it the AcrPull role on the ACR.
Box 2: Use managed identity with role assignment.
you should use a Managed Identity combined with an Azure Role-Based Access Control (RBAC) role assignment to meet the requirement.
Reference:
https://medium.com/@miketobicarter/deploying-a-net-app-to-azure-using-docker-acr-app-service-and-github-actions-step-by-step-3ed6ae8a4d0b
問題 #86
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification. What should you implement?
答案：D
解題說明：
Citation support requires passing document metadata (source name, page/section) through the retrieval step and prompting the model to reference that metadata explicitly in its answer -- this is a pipeline and prompt design pattern, not a sampling parameter.
問題 #87
Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
Hotspot Question
You need to configure vector embedding updates according to the business and technical requirements.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Business Requirements: Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Technical Requirements: Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
Box 1: Change feed processor
The Change feed processor is the correct component to configure for identifying document changes that should trigger vectorization.
This feature in Azure Cosmos DB provides a reliable, distributed, and scalable way to listen to a constant stream of changes (inserts and updates) within a container. It allows you to process these modifications in real-time, making it the perfect trigger for automated workflows like sending new text to an embedding model.
Box 2: Lease Container
The lease container is the component you configure to coordinate and horizontally scale out the change feed-driven vectorization processing across multiple workers or compute instances.
The Lease Container acts as state storage, tracks checkpoints, and distributes partition keys across parallel compute workers (such as Azure Functions or custom change feed processors) to scale out throughput processing safely without duplicate work.
Incorrect:
RU Throughput: Dictates the maximum performance limit, but provisioning RUs alone does not orchestrate or scale out multiple processing workers.
Consistency Level: Affects data replication guarantees and read/write ordering, not parallel processing distribution.
Reference:
https://blog.nashtechglobal.com/managing-record-changes-with-change-feed-in-azure-cosmos-db/
問題 #88
Hotspot Question
You have a web service that is used to pay for food deliveries. The web service uses Azure Cosmos DB as the data store.
You plan to add a new feature that allows users to set a tip amount. The new feature requires that a property named tip on the document in Azure Cosmos DB must be present and contain a numeric value.
There are many existing websites and mobile apps that use the web service that will not be updated to set the tip property for some time.
How should you complete the trigger?
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: getContext().getRequest();
Y: if (!i["tip"]) { (or alternatively: depending on your specific exam question version)Z:
Box 2: if (isNaN(i["tip"]) || i["tip"] === null) {
Box 3: r.setBody(i);
問題 #89
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AI-200考古題: https://www.vcesoft.com/AI-200-pdf.html