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| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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問題 #57
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?
答案：B
問題 #58
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor.
Which three actions should you perform in sequence? To answer, move the appropriate 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) Register the Event Grid resource provider; 2) create a custom topic; 3) create an event subscription.
Detailed Explanation: A newly provisioned subscription must have the Event Grid resource provider available before Event Grid resources can be created. The publisher needs a custom topic as the event-ingress resource, and routing to a downstream processor is then defined by an event subscription on that topic. The event subscription can include filters such as event type or data fields so that only high-confidence events reach the processor. Creating a partner topic or domain is unnecessary for the stated custom workflow.
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 | Create an Event Grid custom topic or domain
問題 #59
You need to configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
答案：B
解題說明：
Detailed Explanation: Azure Key Vault references in App Service settings satisfy the requirement to keep secrets out of container images, source control, and directly stored application configuration. App Service resolves the referenced secret at runtime by using the app identity, so the application can consume the value as a normal setting without embedding credentials in the image. GitHub secrets are build/deployment secrets rather than a runtime App Service secret-delivery mechanism. Dockerfile ENV instructions would place secret material in the image configuration and violate the case requirements.
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 | Use Key Vault references for App Service and Functions | Managed identities for Azure resources
Topic 2, Fabrikam Inc
Background - Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure. The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security. The solution must meet strict performance, scalability, and security requirements. Current environment - Application architecture - The Recommendation engine is a customer- facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA). Embeddings are stored in Azure Database for PostgreSQL by using pgvector. Semantic retrieval uses metadata filtering combined with vector similarity search. Azure Managed Redis is used as a caching layer. Front-end and API workloads are deployed to Azure Container Apps (ACA). Batch model retraining workloads run in Azure Kubernetes Service (AKS). Container and CI/CD - Container images are stored in Azure Container Registry (ACR). CI/CD uses ACR Tasks to build images on commit. ACA environments support revision management. AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring - Logs are collected in Azure Monitor. Teams inspect container logs and Kubernetes events when troubleshooting. Developers write KQL queries to analyze latency spikes. Business requirements - Customer experience: Maintain a seamless, low-latency recommendation experience for end-users, even during unpredictable seasonal traffic spikes. Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs. Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction. Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets. Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers. Technical requirements - Performance: Semantic search latency must remain under 200 milliseconds at peak load. Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput.
Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints. Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization. Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete.
Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates. Identity: Use managed identities for all service-to-service and service-to-database authentication. Plain-text credentials in configuration files are strictly prohibited. Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy. Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero. CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits. Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.
問題 #60
You deploy a production Azure Function app that connects to an Azure SQL Database.
The solution must provide the following functionality:
- Prevent secrets from being exposed in source control.
- Support secret rotation without redeploying the function app.
- Avoid downtime during credential updates.
You need to configure secure and maintainable secret management.
What should you configure?
答案：D
解題說明：
To meet all requirements, you should configure Application settings with Key Vault references.
Source Control Protection: The Function App source code and configuration files only store a reference URI (e.g., @Microsoft.KeyVault(SecretUri=...)) rather than the actual connection string, keeping secrets entirely out of source control.
Seamless Secret Rotation: Azure Key Vault handles secret rotation natively. When a database password changes, you simply update the secret in Key Vault.
Zero Downtime: By using versionless Key Vault references (omitting the specific version GUID from the URI), the Azure Function App will automatically fetch the latest secret version within 24 hours without requiring a code redeployment or app restart.
References:
https://oneuptime.com/blog/post/2026-02-16-how-to-configure-managed-identity-for-azure-app-service-to-access-key-vault-secrets-without-credentials/view
問題 #61
You are designing a messaging solution by using Service Bus for AI document processing.
You need to ensure that a published message is delivered to multiple independent consumers.
Each consumer must receive their own copy of the message.
Which two Service Bus entities should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
答案：A,E
解題說明：
Topics and Subscriptions are the two Azure Service Bus entities that fit this scenario.
Topics: The publisher sends the document processing message to a single topic, which acts as the central distribution hub.
Subscriptions: Each independent consumer creates its own individual subscription under that topic. When a message arrives, a copy is forwarded to every independent subscription so each consumer can process their own copy safely and separately.
Reference:
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-queues-topics-subscriptions
問題 #62
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作為IT業界的頂級公司，Microsoft 通過其認證確定了產品專家的標準，可以說 Microsoft 在業界的聲望和 Microsoft 產品的市場佔有率提升了其認證工程師的含金量，一個 Microsoft 認證工程師獲取在優秀企業工作的機會比普通工程師大60％－80％，平均薪水高出30％-50％。世界500強企業中，有超過2/3的企業選擇了Microsoft電子商務軟體產品作為其核心的運用。因此，獲得AI-200 的證照，即使在強手林立的競爭環境中，你同樣能夠脫穎而出。
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