mediumMultiple Choice
Google ACE Practice Question: A cost-conscious team notices their GKE cluster's…
A cost-conscious team notices their GKE cluster's node pools have consistently high memory utilization (>90%) while CPU remains at 30%. Pods are occasionally OOMKilled. What should they do to balance resource efficiency and stability?
⚠ Common exam trap
Google Cloud often tests the misconception that vertical scaling (VPA) alone can fix memory pressure without considering the node's physical resource ratio, leading candidates to pick Option C and overlook the need for a memory-optimized machine type.
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
✓
Switch node pool machine type to a memory-optimized series (e.g., m2-ultramem) and ensure Pod memory requests are accurate
The team has a memory-bound workload (high memory utilization, low CPU, OOMKills). Switching to a memory-optimized machine series (e.g., m2-ultramem) provides a higher memory-to-CPU ratio, directly addressing the memory pressure. Ensuring accurate Pod memory requests allows the scheduler to place Pods efficiently and prevents overcommitment, balancing resource efficiency with stability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Switch node pool machine type to a memory-optimized series (e.g., m2-ultramem) and ensure Pod memory requests are accurate
Why this is correct
Switching the node pool to a memory-optimized series like m2-ultramem gives each node a much higher RAM-to-vCPU ratio, directly resolving the memory pressure that causes OOMKills. Simultaneously aligning pod memory requests with actual usage lets the scheduler pack pods according to real footprint, and enables cluster autoscaler to add the right capacity. These changes together correct the capacity deficit.
- ✗
Increase CPU limits for all Pods to use the available CPU capacity
Why it's wrong here
These OOMKills stem from insufficient memory, not CPU. Raising CPU limits merely caps CPU usage and doesn't alter memory requests or limits, so the kernel will still kill pods when node memory is exhausted. You must either increase memory resources or provide nodes with more RAM.
- ✗
Enable vertical pod autoscaling (VPA) set to Recreate mode as the only change
Why it's wrong here
VPA in Recreate mode can only adjust pod resource requests and restart pods to apply them, but it cannot change the underlying node pool's memory capacity. If every node is already exhausted, even properly-sized pods will fail to schedule. This approach also causes downtime and is complementary to, not a substitute for, selecting a memory-optimized machine type.
- ✗
Reduce the number of replica Pods to lower memory consumption
Why it's wrong here
Reducing replicas lowers aggregate memory demand but truncates service capacity and availability, which is not an acceptable fix for a capacity deficit. The cluster must be provisioned with more total memory, not hosting fewer workloads. It also doesn't address the per-node memory pressure if the remaining pods still fit poorly on the existing node shape.
Go deeper
Related to this question
Learn chapter
GKE Node Pools and Cluster Autoscaler
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
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the ACE exam.