Câu 6: AI-200: Developing AI Cloud Solutions on Azure

HOTSPOT -This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study. To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study. A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it. To start the case study -To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the "Question" button to return to the question. 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 ArchitectureMicroservices 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 NoSQ L. 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. You need to configure image build automation based on the technical requirements. Which settings should you configure? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.

1. A. Lựa chọn A bằng hình ảnh — đáp án hiện tại