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Google Associate Cloud Engineer Exam - 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?

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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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Roosevelt
6 months ago
Not sure about that nodeSelector thing, sounds complicated.
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Onita
6 months ago
Definitely go with D, it’s the most efficient way!
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Ernie
6 months ago
Wait, do we really need a whole new cluster for just one team?
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Hailey
7 months ago
I think recreating all nodes (Option B) is overkill.
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Yuette
7 months ago
Option D is the best choice for adding GPU support.
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Shayne
7 months ago
Creating a separate cluster for the ML team sounds like a lot of overhead. I hope that's not the best option here.
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Shantay
7 months ago
I'm leaning towards option D, but I feel like I need to double-check if the nodeSelector syntax is correct.
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Nadine
7 months ago
I think recreating all the nodes seems excessive. We practiced a question where we just added a new node pool instead.
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Terrilyn
8 months ago
I remember something about adding annotations for GPU access, but I'm not sure if it's just for pods or if it applies to the whole cluster.
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Nieves
8 months ago
I'm not sure about the "accelerator" annotation - does that actually work with GKE? I'd want to double-check the documentation on that one.
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Golda
8 months ago
Okay, I think I've got it. We need to add a new node pool with the right GPU hardware and then have the ML team target those nodes. That should be the most efficient approach.
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Wendell
8 months ago
Hmm, I'm a bit confused. Do we need to create a whole new cluster just for the ML team? That seems like a lot of extra work.
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Cyril
8 months ago
This seems pretty straightforward. I think the key is to add GPU-enabled nodes to the cluster without having to recreate the entire thing.
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Jolene
8 months ago
I'm pretty confident the answer is D. Combining the administrative services seems like the most effective way to lower costs with 24-hour coverage.
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Tequila
8 months ago
I've got this! The valid WebSocket message types are Binary Message and Ping Message. I'm feeling good about this one.
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Stevie
1 year ago
Looks like the ML team is about to get a serious GPU boost. Time to start my own AI side hustle!
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Kaitlyn
1 year ago
I wonder if the ML team will ask for the latest and greatest GPUs next... Gotta stay ahead of the curve!
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Gianna
11 months ago
I wonder if the ML team will ask for the latest and greatest GPUs next... Gotta stay ahead of the curve!
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Odette
11 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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Yuonne
11 months ago
C) Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
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Elden
11 months ago
A) Ask your ML team to add the ''accelerator: gpu'' annotation to their pod specification.
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Agustin
1 year ago
Adding a new, GPU-enabled node pool to the existing GKE cluster and using the nodeSelector seems like the best option to me. It's targeted and cost-effective.
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Tayna
1 year ago
Creating a dedicated Kubernetes cluster with GPU-enabled nodes sounds like a good idea, but it might be overkill for a non-production workload.
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Tammy
1 year ago
Recreating all the nodes to enable GPUs on all of them sounds like a lot of effort and cost. I don't think that's the most efficient approach.
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Julianna
12 months ago
A) That sounds like a more efficient way to provide access to GPUs for the ML team without recreating all the nodes.
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Diane
12 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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Francoise
12 months ago
A) Ask your ML team to add the ''accelerator: gpu'' annotation to their pod specification.
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Tasia
1 year ago
I'm not sure, I think option C could also work. Creating a separate Kubernetes cluster dedicated to the ML team might simplify management.
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Alecia
1 year 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 year 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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Tomas
1 year ago
A) Ask your ML team to add the ''accelerator: gpu'' annotation to their pod specification.
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Oliva
1 year ago
I agree with Owen. Option D seems like the most efficient way to provide the ML team with the GPUs they need.
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Owen
1 year ago
I think option D is the best choice. It allows us to add GPU-enabled nodes to the existing GKE cluster.
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