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GCP Associate Cloud Engineer Practice Question

You administer a standard GKE cluster in us-central1 that has a single default node pool of n1-standard-4 VMs. A new containerized batch workload requires NVIDIA T4 GPUs for occasional processing bursts. The workload must not evict existing Pods that do not need GPUs, and you want the GPU capacity to scale to zero when no GPU jobs are queued to minimize cost. Which approach satisfies these requirements with the least disruption to current workloads?

  • Turn on cluster autoscaling for the default node pool, set the minimum size to zero, and deploy the GPU workload as normal Pods without special scheduling rules.

  • Create a new node pool that uses an n1-standard-4 machine type with one nvidia-tesla-t4 accelerator, add a NoSchedule taint to the pool, add matching tolerations to the batch Pods, and enable autoscaling with 0-1 nodes.

  • Update the existing default node pool to attach NVIDIA T4 GPUs and set its autoscaler to a 0-1 node range.

  • Package the batch workload as a container image and deploy it to Cloud Run, which automatically provisions GPU instances on demand.

GCP Associate Cloud Engineer
Ensuring successful operation of a cloud solution
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