Google Exam Associate Cloud Engineer Topic 2 Question 88 Discussion

Actual exam question for Google's Associate Cloud Engineer exam

Question #: 88
Topic #: 2

[All Associate Cloud Engineer Questions]

You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can run non-production workloads. Your Machine Learning (ML) team needs access to Nvidia Tesla P100 GPUs to train their models. You want to minimize effort and cost. What should you do?

AAsk your ML team to add the ''accelerator: gpu'' annotation to their pod specification.

BRecreate all the nodes of the GKE cluster to enable GPUs on all of them.

CCreate your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.

DAdd a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

Show Suggested Answer

Suggested Answer: A

A managed instance group (MIG) is a group of identical virtual machines (VMs) that you can manage as a single entity. You can use a MIG to deploy and maintain a stateless application that runs directly on VMs. A MIG can automatically scale the number of VMs based on the load or a schedule. A MIG can also automatically heal the VMs if they become unhealthy or unavailable. A MIG is suitable for applications that need to run on VMs rather than containers or serverless platforms.

B is incorrect because Kubernetes Engine is a managed service for running containerized applications on a cluster of nodes. It is not necessary to use Kubernetes Engine if the application does not use containers and can run directly on VMs.

C is incorrect because Cloud Functions is a serverless platform for running event-driven code in response to triggers. It is not suitable for applications that need to run continuously and handle HTTP requests.

D is incorrect because Cloud Run is a serverless platform for running stateless containerized applications. It is not suitable for applications that do not use containers and can run directly on VMs.

Managed instance groups documentation

Choosing a compute option for Google Cloud

by Cristina at Jul 15, 2024, 10:31 PM

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2 months ago

The 'accelerator: gpu' annotation seems like a quick and easy solution, but I'm not sure if that will work for the specific Nvidia Tesla P100 GPUs.

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Irene

1 months ago

D) Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

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