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Google Associate Cloud Engineer Exam - Topic 1 Question 80 Discussion

A team of data scientists infrequently needs to use a Google Kubernetes Engine (GKE) cluster that you manage. They require GPUs for some long-running, non-restartable jobs. You want to minimize cost. What should you do?
B) Create a VerticalPodAutscaler for those workloads.
A) Enable node auto-provisioning on the GKE cluster.
C) Create a node pool with preemptible VMs and GPUs attached to those VMs.
D) Create a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1.

Google Associate Cloud Engineer Exam - Topic 1 Question 80 Discussion

Actual exam question for Google's Associate Cloud Engineer exam
Question #: 80
Topic #: 1
[All Associate Cloud Engineer Questions]

A team of data scientists infrequently needs to use a Google Kubernetes Engine (GKE) cluster that you manage. They require GPUs for some long-running, non-restartable jobs. You want to minimize cost. What should you do?

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Suggested Answer: B

by Judy at Apr 21, 2024, 10:12 AM

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Lenna
7 months ago
VerticalPodAutoscaler is cool, but not for GPU jobs, right?
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Lenny
8 months ago
Node auto-provisioning might not save enough costs.
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France
8 months ago
Surprised that preemptible VMs can handle long-running jobs.
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Mona
8 months ago
I think autoscaling is a better option for flexibility.
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Ling
8 months ago
Preemptible VMs are way cheaper!
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Melissia
9 months ago
I'm a bit confused about the VerticalPodAutoscaler; I thought it was more for optimizing resource requests rather than cost-saving.
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Earleen
9 months ago
I practiced a similar question where we had to choose between autoscaling and preemptible VMs. I feel like option C might be the right choice.
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Arleen
9 months ago
I think enabling node auto-provisioning could help, but it might not be the most cost-effective for infrequent jobs.
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Carol
9 months ago
I remember we discussed using preemptible VMs to save costs, but I'm not sure if that's the best option here.
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Matthew
9 months ago
I'm pretty confident that option C is the best choice here. Preemptible VMs with GPUs should give us the cost savings we're looking for while still meeting the data scientists' requirements.
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Donte
9 months ago
Hmm, I'm a bit unsure about this one. I'm leaning towards option D, as it seems to provide the flexibility of autoscaling the node pool with GPUs. But I'll need to think it through a bit more.
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Reita
9 months ago
This seems like a tricky question, but I think option C is the way to go. Using preemptible VMs with GPUs should help minimize costs while still providing the necessary resources.
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Domingo
9 months ago
Option A with node auto-provisioning sounds like a good way to automatically scale up the cluster as needed. That could be a simple and effective solution to the problem.
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Sarah
9 months ago
Hmm, I'm a little unsure about this one. I know the question is about workforce education, but I'm not totally confident that Security Awareness and Training is the right answer. I might need to re-read the question and options a few times to make sure I'm picking the best choice.
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Justine
9 months ago
This seems like a tricky one, but I'll give it my best shot. I'll start by eliminating any options that don't seem to fit the description of corrosion from oxygen.
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Phyliss
10 months ago
Okay, I remember learning about VLANs in class. I believe the key is that they create logical network segments, which is what option B describes. I'm pretty confident that's the right answer.
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Howard
10 months ago
I remember it has something to do with maintaining secure connections, but I keep mixing up how it relates to WAN Edge routers and vSmart.
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Dorothea
1 year ago
If I had a penny for every time I've had to optimize for cost on a certification exam, I'd be rich enough to buy my own GKE cluster.
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Lynna
1 year ago
A) Enable node auto-provisioning on the GKE cluster.
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Dorian
1 year ago
D) Create a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1.
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Karima
1 year ago
C) Create a node pool with preemptible VMs and GPUs attached to those VMs.
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Justine
1 year ago
Wait, there's an option to use preemptible VMs with GPUs? That's like the holy grail of cost optimization!
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Ngoc
1 year ago
I didn't know that was an option, thanks for pointing it out!
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Carman
1 year ago
That's right! It's a great way to minimize costs while still getting the GPU power you need.
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Georgene
1 year ago
Yes, option C allows you to create a node pool with preemptible VMs and GPUs attached to those VMs.
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Rozella
1 year ago
A and B don't really address the GPU requirement, so I'm leaning towards C. Gotta love those preemptible VMs!
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Nikita
1 year ago
D sounds tempting, but I'd rather not have to worry about scaling up and down. C is the clear winner here.
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Chan
1 year ago
It might be more convenient, but C is still the most cost-effective choice.
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Kris
1 year ago
But wouldn't D be more convenient in terms of autoscaling?
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Arlene
1 year ago
I agree, using preemptible VMs with GPUs attached seems like the most cost-effective solution.
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Lorriane
1 year ago
I think C is the best option for minimizing cost.
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Narcisa
1 year ago
That's a good point. Autoscaling with GPUs could be more cost-effective in the long run.
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Alisha
1 year ago
I disagree, I believe option D is better. Autoscaling with GPUs can ensure the jobs are completed efficiently.
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Allene
1 year ago
Hmm, I think C is the way to go. Preemptible VMs with GPUs? That's a cost-saver for sure!
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Barabara
1 year ago
Yes, it's a great way to minimize cost while still meeting the data scientists' needs.
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Tamar
1 year ago
I agree, using preemptible VMs with GPUs attached is a cost-effective solution.
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Narcisa
1 year ago
I think option C is the best choice. Preemptible VMs with GPUs can help minimize cost.
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