Google PCA Manage implementation of cloud architecture Practice Question
Exhibit
Refer to the exhibit. ``` $ gcloud container clusters describe my-cluster --zone us-central1-a --format 'table(name, nodeConfig.machineType, nodePools[].initialNodeCount, nodePools[].config.machineType)' NAME: my-cluster MASTER_VERSION: 1.28.5-gke.2000 NODE_CONFIG_MACHINE_TYPE: e2-standard-4 NODE_POOLS: [0].initialNodeCount: 3 NODE_POOLS: [0].config.machineType: e2-standard-4 ```
A developer runs the command above and sees the output. The cluster has one node pool with 3 nodes, each of type e2-standard-4 (4 vCPU, 16 GB RAM). The application requires at least 2 GB of memory per pod and the cluster has 10 pods that need to be scheduled. The developer also notices that the node pool autoscaling is enabled with a minimum of 1 and maximum of 5 nodes. However, the cluster is unable to schedule all pods. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between pod resource requests and limits, and the fact that the Cluster Autoscaler scales based on requests, not limits, leading candidates to overlook system reservations or assume autoscaling is misconfigured.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The pod resource requests exceed the allocatable resources on the existing nodes after accounting for system reservations.
The cluster has 3 e2-standard-4 nodes, each with 4 vCPU and 16 GB RAM. After accounting for system reservations (e.g., kubelet, OS, daemonsets), the allocatable memory per node is typically around 13-14 GB. With 10 pods each requesting 2 GB, the total memory request is 20 GB, but the total allocatable memory across 3 nodes is only about 39-42 GB. However, the autoscaler can only scale up to 5 nodes, and even then, the total allocatable memory would be around 65-70 GB, which is sufficient. The most likely cause is that the pod resource requests exceed the allocatable resources on the existing nodes, preventing scheduling, and the autoscaler may not have triggered yet or is constrained by other factors like CPU or node limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cluster is running an older version of Kubernetes that does not support node auto-scaling.
Why it's wrong here
Version 1.28.5 supports node auto-scaling.
- ✗
The node pool autoscaler is not properly configured to scale up based on pod resource requests.
Why it's wrong here
The autoscaler is enabled and should scale up if pods are unschedulable due to resource constraints.
- ✗
The node auto-repair feature is disabled, causing a node to be unhealthy.
Why it's wrong here
Node auto-repair does not affect scheduling of new pods.
- ✓
The pod resource requests exceed the allocatable resources on the existing nodes after accounting for system reservations.
Why this is correct
System reservations (kube-reserved, eviction threshold) reduce allocatable CPU and memory, and the pod requests may exceed what is available.
Go deeper
Related to this question
Learn chapter
Google Cloud Resource Hierarchy and Organization
Key term
Pod
A pod is the smallest deployable unit in Kubernetes, containing one or more containers that share storage, network, and a specification for how to run.
Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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