as the instances (normal or preemptible) would be terminated and relaunched if the health check fails either due to application not configured properly or the instances firewall do not allow health check to happen.
GCP provides health check systems that connect to virtual machine (VM) instances on a configurable, periodic basis. Each connection attempt is called a probe. GCP records the success or failure of each probe.
Health checks and load balancers work together. Based on a configurable number of sequential successful or failed probes, GCP computes an overall health state for each VM in the load balancer. VMs that respond successfully for the configured number of times are considered healthy. VMs that fail to respond successfully for a separate number of times are unhealthy.
GCP uses the overall health state of each VM to determine its eligibility for receiving new requests. In addition to being able to configure probe frequency and health state thresholds, you can configure the criteria that define a successful probe.

Reference: https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/go/config/appref
"App Engine calculates the number of instances necessary to serve your current application traffic based on scaling settings such as target_cpu_utilization and target_throughput_utilization. Setting min_idle_instances specifies the number of instances to run in addition to this calculated number. For example, if App Engine calculates that 5 instances are necessary to serve traffic, and min_idle_instances is set to 2, App Engine will run 7 instances (5, calculated based on traffic, plus 2 additional per min_idle_instances)." Automatic scaling creates dynamic instances based on request rate, response latencies, and other application metrics. However, if you specify the number of minimum idle instances, that specified number of instances run as resident instances while any additional instances are dynamic.
Ref: https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed

The goal is to create a serverless, scalable, and asynchronous transaction processing system with minimal management overhead.
Serverless Requirement:Options involving installing Kafka on VMs (A, B) or using VM instances for processing (B, C) introduce management overhead associated with VMs (patching, scaling configuration, OS management) and Kafka cluster management, violating the serverless and minimal management criteria.
Asynchronous Requirement:Both Kafka and Pub/Sub can handle asynchronous messaging. However, Pub
/Sub is Google Cloud's fully managed, serverless messaging service, inherently minimizing management overhead compared to self-managed Kafka on VMs.
Scalability and Processing:Cloud Run is a fully managed, serverless platform that automatically scales based on traffic, suitable for processing transactions without managing underlying infrastructure. VM instances require manual scaling configuration or managed instance groups, adding overhead.
Combining Pub/Sub for asynchronous message ingestion (fully managed, serverless) and Cloud Run for processing (fully managed, serverless, scalable) directly meets all requirements: serverless, scalable, asynchronous, and minimal management overhead. Option D is the only one that uses fully serverless components for both ingestion and processing.
References:
Google Cloud Pub/Sub Overview: "Pub/Sub is an asynchronous and scalable messaging service..." -
https://cloud.google.com/pubsub/docs/overview
Google Cloud Run Overview: "Cloud Run is a managed compute platform that lets you run containers directly on top of Google's scalable infrastructure." -https://cloud.google.com/run/docs/overview/what-is- cloud-run Serverless Patterns (Pub/Sub + Cloud Run): This combination is a standard pattern for event-driven, serverless applications. -https://cloud.google.com/run/docs/triggering/pubsub-push