https://kubernetes.io/docs/concepts/overview/components/

https://kubernetes.io/docs/concepts/workloads/

For web applications with unpredictable traffic patterns, dynamic scaling and load optimization are crucial for cost management. Horizontal Pod Autoscaler (HPA) with CPU and memory utilization metrics automatically scales the deployment up and down based on actual load, effectively managing resources during spikes. Implementing a caching layer reduces load on the backend servers, further reducing resource consumption and costs. Manually scaling is inefficient, and resource quotas don't address the dynamic nature of traffic spikes. While serverless functions offer scaling, they might not be suitable for all web application components.

Namespaces provide isolated environments within a Kubernetes cluster. By creating separate namespaces for development, testing, and production, you can test new features in a controlled environment without impacting production.

Both options B and D provide effective solutions for identifying memory-intensive containers. Option B allows you to directly analyze the 'kube_pod_container_status_memory_usage_bytes• metric, which provides the actual memory usage of each container within the pod. Option D suggests filtering Prometheus queries by container name and sorting by memory usage, enabling you to easily pinpoint containers with the highest memory consumption. While option A provides information about requested memory limits, it doesn't directly reflect the actual memory usage. Options C and E are not directly relevant to identifying memory-intensive containers.