A container image is a blueprint for creating a container, while a container runtime is the software that executes the container. A container image contains all the necessary components, libraries, and dependencies to run an application in a container. It's a snapshot of a specific point in time. Container runtimes, such as Docker, containerd, or CRI-O, are responsible for loading the image, creating a container, and managing its lifecycle. They provide the environment and resources for the container to run.

Several approaches can help maintain state during scaling for stateful applications: StatefulSets (A) manage stateful applications by providing persistent storage and stable network identities for Pods- Custom scaling logic (C) allows you to implement specific scaling strategies that preserve state, such as graceful shutdowns and data synchronization. Persistent volume claims (PVCs) (D) ensure that the application's data is stored on persistent volumes, preserving it even during scaling events. While options B and E might seem appealing, they are not practical solutions. Scaling during idle periods might not cover all scenarios, and disabling scaling altogether eliminates the benefits of autoscaling.

The kube-apiserver component acts as the central control plane for Kubernetes. It handles all communication and requests from other components, including authentication and authorization. It enforces security policies by verifying credentials and granting access based on configured rules.

The most effective approaches to handle workload spikes are implementing a message queue (like SQS or SNS) to buffer requests and enabling concurrency settings on Lambda functions. Option A increases memory but doesn't address scalability. Option C is not relevant to Lambda auto-scaling. Option D is a deployment tool, not directly related to handling workload spikes. Option E directly enables parallel processing, making it a suitable solution.

A Service is a Kubernetes resource that provides a stable endpoint for a group of Pods. It acts as a load balancer, distributing traffic across the Pods in a round-robin manner by default. This ensures that all Pods receive an equal share of the incoming traffic.