Exam Professional Cloud Architect topic 1 question 119 discussion

Should be B, you want to minimize costs. https://cloud.google.com/dataproc/docs/concepts/compute/secondary-vms#preemptible_and_non-preemptible_secondary_workers

It's A, the primary workers can only be standard, where secondary workers can be preemtible.------In addition to using standard Compute Engine VMs as Dataproc workers (called "primary" workers), Dataproc clusters can use "secondary" workers. There are two types of secondary workers: preemptible and non-preemptible. All secondary workers in your cluster must be of the same type, either preemptible or non-preemptible. The default is preemptible.

Probably A - Primary workers must be standard - Preemptible doesn't always save cost - Infrastructure on prem doesn't have spot machines - You cannot choose spot/preemptible when creating the cluster, only when provisioning secondary nodes, which are actually preemptible by default. - They do not store data, only do data processing

B: being a multiple-choice question, we need to focus on explicit keywords here. Management effort => Managed service -> Dataproc. Cost-optimization => Preemptible. For ones who say "but that also requires fault toleration": well, there is no explicit keyword in the question says "we have critical jobs" or "out data scientists team has not takes into account toleration". So we must not assume that's needed.

I will go with A , reason preemptible instances are unpredictable and there is no mention of work criticallity. So my answer is A against B

A - only secondary workers can be preemptible and "Using preemptible VMs does not always save costs since preemptions can cause longer job execution with resulting higher job costs" according to: https://cloud.google.com/dataproc/docs/concepts/compute/secondary-vms#preemptible_and_non-preemptible_secondary_workers

Agree with B

Answer is B. The secondary worker type instance for default Dataproc cluster is preemptible VMs. https://cloud.google.com/dataproc/docs/concepts/compute/secondary-vms

Dataproc: Dataproc is a fully managed Apache Spark and Hadoop service on Google Cloud Platform. It allows you to run clusters without the need to manually deploy and manage Hadoop clusters on Compute Engine. Preemptible Worker Instances: Preemptible instances are short-lived, cost-effective virtual machine instances that are suitable for fault-tolerant and batch processing workloads. Since Hadoop jobs can often tolerate interruptions, using preemptible instances can significantly reduce costs. Option B leverages the benefits of Dataproc for managing Hadoop clusters without the need for manual deployment and takes advantage of preemptible instances to minimize costs. This aligns well with the goal of minimizing both costs and infrastructure management efforts.

Using preemptible VMs does not always save costs since preemptions can cause longer job execution with resulting higher job costs

Using standard Compute Engine VMs as Dataproc workers (called "primary" workers), Preemptible can be only used for secondary workers hence A is valid answer

minimize costs -> preemtipble

You want to minimize costs and infrastructure management effort > B

"You want to minimize costs and infrastructure management effort" -> B

It is A

Answer should be B, because minizing the costs is wanted.

A is the correct response. Per documentation "You can gain low-cost processing power for your jobs by adding preemptible worker nodes to your cluster. These nodes use preemptible virtual machines." The focus of the question is to reduce cost, hence preempttible VM works best