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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 93
You are reviewing the message processing code in a backend worker service.
You review the following Python code that initializes and starts a Service Bus processor
Answer:
Explanation:
Explanation:
Verified Answer: No; Yes; No.
Detailed Explanation: The default Service Bus receive behavior is PeekLock rather than receive-and-delete, so merely receiving a message does not remove it from the queue; it must be settled successfully. If processing fails and the message remains unsettled or the lock is lost, it can become available for another delivery. Invalid JSON, however, is an application-level content problem: Service Bus does not inspect the payload as JSON and automatically retry it to MaxDeliveryCount solely because deserialization fails. The application's settlement/error path determines what happens.
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 | Service Bus message transfers, locks, and settlement | Service Bus messages, routing, and correlation
NEW QUESTION # 94
You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?
Answer: C
Explanation:
The batch embedding workload is planned to run in Azure Kubernetes Service (AKS) , and Kubernetes workloads should be defined declaratively by using YAML manifest files . Microsoft states that a Kubernetes manifest defines the desired state of cluster resources, including Deployments, Jobs, Services, ConfigMaps, and other Kubernetes objects. These manifests can then be deployed by using kubectl apply -f < manifest > .
yaml.
For this case, YAML is particularly appropriate because the technical requirements emphasize repeatable, version-controlled, automated deployments . A batch vector embedding regeneration process would typically be represented as a Kubernetes workload such as a Job or another batch-oriented resource, with its configuration committed to Git alongside the rest of the deployment definitions.
kubectl run and kubectl create are imperative commands and are less appropriate for a controlled Git-based deployment model because they do not inherently preserve the complete desired-state definition as source- controlled configuration. XML is not the standard Kubernetes manifest format. az aks commands are primarily used to provision and manage the AKS cluster itself rather than declaratively define application workloads running inside the cluster.
Therefore, the correct choice is YAML-formatted Kubernetes manifest files .
Study Guide references: Azure Kubernetes Service # Kubernetes manifests; declarative deployments; kubectl apply; Git-based workload configuration.
NEW QUESTION # 95
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.
Answer:
Explanation:
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.
NEW QUESTION # 96
A company is developing a solution that allows smart refrigerators to send temperature information to a central location.
The solution must receive and store messages until they can be processed. You create an Azure Service Bus instance by providing a name, pricing tier, subscription, resource group, and location.
You need to complete the configuration.
Which Azure CLI or PowerShell command should you run?
Answer: A
NEW QUESTION # 97
You are troubleshooting a production Azure OpenAI service that is experiencing intermittent connectivity issues. All telemetry is sent to a Log Analytics workspace.
You must write a KQL query that analyzes the AppRequests table. The query must filter for requests that occurred within the last 30 minutes and have a failed status.
The final output must display the total number of failures for each unique operation.
You need to select the KQL operators required to filter the time range and aggregate the failure counts.
Which two operators should you use? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,D
NEW QUESTION # 98
......
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