ExpertMedium1 markMultiple Choice
GCP ACE · Question 35 · Domain 4.2: Managing GKE resources
Your team has an existing GKE standard cluster running web applications. A new data science team wants to deploy machine learning workloads to the same cluster, but their containers require GPUs. The current cluster nodes do not have GPUs attached.
What is the BEST way to accommodate this new workload?
Your team has an existing GKE standard cluster running web applications. A new data science team wants to deploy machine learning workloads to the same cluster, but their containers require GPUs. The current cluster nodes do not have GPUs attached.
What is the BEST way to accommodate this new workload?
Answer options:
A.
Add a new node pool to the existing cluster using machine types with attached GPUs.
B.
Edit the existing node pool to add GPUs to the running instances.
C.
SSH into the existing GKE nodes and manually install GPU drivers.
D.
Delete the entire GKE cluster and recreate it with GPU-enabled nodes.
How to approach this question
Understand the concept of GKE Node Pools. They allow you to mix different VM types within the same cluster.
Full Answer
A.Add a new node pool to the existing cluster using machine types with attached GPUs.✓ Correct
Add a new node pool to the existing cluster using machine types with attached GPUs.
A GKE cluster can contain multiple 'node pools'. A node pool is a subset of node instances within a cluster that all have the same configuration (machine type, disk size, GPUs). To add GPU capabilities to an existing cluster without disrupting current workloads, you simply create a new node pool configured with GPUs. You can then use Kubernetes node selectors or taints/tolerations to ensure the ML workloads run only on the GPU nodes.
Common mistakes
Believing you can edit an existing node pool (they are immutable) or thinking you must recreate the whole cluster.
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