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ASSOCIATE-CLOUD-ENGINEER · Question #106
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…
The correct answer is D. Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the. Before using GPUs on GKE, keep in mind the following limitations: You cannot add GPUs to existing node pools. GPU nodes cannot be live migrated during maintenance events. https://cloud.google.com/kubernetes-engine/docs/how-to/gpus
Submitted by carter_n· Mar 30, 2026Deploying and implementing a cloud solution
Question
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?
Options
- 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.
- DAdd a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the
How the community answered
(16 responses)- A6% (1)
- B13% (2)
- C6% (1)
- D75% (12)
Explanation
Before using GPUs on GKE, keep in mind the following limitations: You cannot add GPUs to existing node pools. GPU nodes cannot be live migrated during maintenance events. https://cloud.google.com/kubernetes-engine/docs/how-to/gpus
Topics
#GKE node pool#GPU#ML workloads#preemptible nodes
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