我們Fast2test免費更新我們研究的培訓材料，這意味著你將隨時得到最新的更新的AI-200考試認證培訓資料，只要AI-200考試的目標有了變化，我們Fast2test提供的學習材料也會跟著變化，我們Fast2test知道每個考生的需求，我們將幫助你通過你的AI-200考試認證，以最優惠最實在的價格和最高超的品質來幫助每位考生，讓你們順利獲得認證。
| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Topic 2: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement computer vision solutions |
| Topic 3: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
我們Fast2test Microsoft的AI-200考試的試題及答案，為你提供了一切你所需要的考前準備資料，關於Microsoft的AI-200考試，你可以從不同的網站或書籍找到這些問題，但關鍵是邏輯性相連，我們的試題及答案不僅能第一次毫不費力的通過考試，同時也能節省你寶貴的時間。
問題 #73
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must retrieve the five most similar documents to an input embedding.
You need to construct the similarity query.
What should you do?
答案：C
解題說明：
To retrieve the five most similar documents from Azure Cosmos DB for NoSQL using vector search, you must limit the number of results.
When performing vector similarity searches using the VectorDistance() function, you must restrict the output size directly in the query.
Reference:
https://stemhash.com/semantic-search-with-llms/
問題 #74
You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
答案：A,C
解題說明：
The two appropriate consistency levels for a globally distributed AI application needing to view writes within a predictable time window are Bounded Staleness and Strong.
Bounded Staleness: This provides a guarantee that reads will lag behind writes by a user- specified "staleness window" (either in time or number of operations). It delivers high availability and predictable read consistency across all regions, balancing performance with strict read-order guarantees.
Strong: This provides linearizable and absolute consistency, ensuring that writes are seen synchronously across all regions. It features an exact zero-second predictable time window, though it comes at the expense of higher latency for multi-region operations due to the time required to replicate globally.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
問題 #75
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome 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.
答案：
解題說明：
Explanation:
問題 #76
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.
答案：
解題說明：
Explanation:
Verified answer: 1) Enable ingress; 2) add the required DNS records; 3) validate ownership of the custom domain; 4) add the custom domain to the Container App; 5) bind the certificate.
Detailed Explanation: A custom HTTPS hostname requires an ingress endpoint, DNS proof that the requester controls the hostname, the hostname association itself, and a TLS certificate bound to that hostname.
DNS records must exist before ownership validation can succeed. Once ownership is validated, the domain can be associated with the Container App and the certificate can be bound to provide HTTPS. Skipping ingress or DNS validation would prevent the hostname from being correctly routed and secured.
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 | Custom domains and certificates in Container Apps
Topic 1, Proseware Inc. Case Study
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.
問題 #77
You are designing a solution that will use two Azure Functions apps: App1 and App2. App1 is Windows based and will be deployed as code. App2 is Linux based and will be deployed as a container image.
Estimates show that the duration of the request processing for both apps will range from 1 to 10 minutes.
You plan to implement App1 and App2 by using the hosting plan to satisfy the following requirements:
* Request processing can complete within the estimated time range.
* The autoscaling behavior is event driven.
* The upper scaling limit is maximized.
You need to create the hosting plan for the implementation.
Which hosting plan should you create? To answer, move the appropriate hosting plans to the correct apps.
You may use each hosting plan once, more than once, or not at all. You may..
答案：
解題說明：
Explanation:
pp1: Consumption. App2: Premium.
Detailed Explanation: For App1, the Windows code-based Consumption plan satisfies event-driven autoscaling, allows execution up to the stated ten-minute range, and has the highest scaling ceiling among the listed event-driven choices for this workload. App2 is deployed as a Linux container image; among Premium, Dedicated, and Consumption, Premium is the event-driven plan that supports Linux containerized Functions and permits long-running execution. Dedicated hosting is not the requested event-driven autoscale model. The original Premium/Premium answer therefore did not maximize App1's upper scaling limit.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions scale and hosting
問題 #78
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