Microsoft Updated AI-200 Exam Questions and Answers by emre

Verified Answer: Single URL for test users: Revision label. Current microservice activation behavior: Revision mode.

Detailed Explanation: Azure Container Apps revision labels provide a stable label-specific URL that can be reassigned to a different revision, which is appropriate for a consistent test endpoint. Revision mode controls whether one or multiple revisions can be active at the same time and therefore governs how the current and updated application versions are activated. The container image and registry identify what is deployed, but they do not provide the stable test URL or the application-level revision activation behavior requested.

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 | Azure Container Apps revisions | Azure Container Apps revision labels

Detailed Explanation: Azure Container Apps uses KEDA-based scale rules for event-driven workloads. A Service Bus scaler can monitor queue depth and create replicas only when messages are pending. To meet the explicit cost requirement, the scale configuration must permit a minimum replica count of zero so all active replicas can terminate when the queue is empty. Increasing the maximum replica count changes peak capacity, and HTTP ingress concurrency is not the correct scaling signal for a background Service Bus consumer.

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 | Set scaling rules in Azure Container Apps

The code configures the tracer provider before any spans are created: Yes

The configuration exports traces synchronously: No

The configuration enables export to Azure Monitor: Yes

The first statement is Yes. The code calls trace.set_tracer_provider(TracerProvider()) before obtaining the tracer and before any span-creation operation occurs. In OpenTelemetry, retrieving a tracer by using trace.get_tracer() does not itself create a span; spans are created later through methods such as start_span() or start_as_current_span().

The second statement is No. The code uses BatchSpanProcessor, which batches completed spans and exports them through a background processing mechanism rather than exporting every span synchronously on the application ' s execution path. OpenTelemetry documentation states that BatchSpanProcessor batches ended spans before sending them to the configured exporter, and the Python implementation uses a background thread for export processing.

The third statement is Yes. AzureMonitorTraceExporter is instantiated with an Azure Monitor/Application Insights connection string and passed to BatchSpanProcessor. That processor is then registered with the tracer provider. Microsoft provides this same pattern specifically for exporting OpenTelemetry traces to Azure Monitor/Application Insights.

Study Guide references: Azure Monitor OpenTelemetry; TracerProvider; BatchSpanProcessor; AzureMonitorTraceExporter; distributed tracing.