Exam Professional Cloud Architect topic 1 question 67 discussion

C is correct, need "ifnotpresent"when uploads to container registry

C is the best choice. You can create a k8s cluster with just one node and use a different namespaces for staging and production. In staging, you will test the changes

Answer is C

should be option C because if you are working in real world, GKE is the best solution for such a case. Furthermore, its reliable, scalable, flexible, at least the best option among the other three.

Ngl it's A. Don't use GKE, it won't schedule the deployment as most of the resources already occupied by kube-system

imagePullPolicy: Always ensures that the latest version of the image is always pulled, which guarantees that the most recent code is deployed. Restarting pods ensures that the new version is deployed without requiring manual intervention.

B because Kubernetes will be overkill and A is not reliable

A is wrong as after the restart the script will be rerun and fetch the code directly from the repo (even if production). The load of the massive number of dependencies will take a lot of timee, and the application version will be fuzzy.

C is correct, B (instance template cannote be updated once created.

C - Correct

The correct answer is C. Because it is the only option that RELIABLY tests the app in staging before it is applied to production. Remember that one of the requirements in the question is to reliably deploy the app.

You don't need GKE for 0.1 CPU, only A meet hte needs

For 0.1 CPU I will never use GKE, considering the associated cost with control plane and not even one option in the question mentioning micro instances for the node pool

When we read the question - "0.1 CPU cores and 128 MB of memory" to operate in production. You want to monitor and "maximize machine utilization"... Answer A should be a fit based on the question details. Would GKE for tiny application be overkill?

python app on compute engine is a disastrous architecture. C is the correct architecture which tests the app before putting to prod

You should use GKE, because your can scale up and down based on your demand. Also you can specifiy the resource size like 0.1 CPU and 128 MB of memory per Pod. Secondly, Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to production namespace after testing. is best practice.

f1 micro managed instance group fits all the requirements, with capability to run upto 2 instances per per for given requirement GKE cluster would be an overkill, with control plane nodes/cost not even being considered. n1-standard-1 instance would require 10 application instances/pod to be running (assuming 0 ds) and would still be leaving 75% memory unused