Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
A retail company runs its order-processing system on Google Kubernetes Engine (GKE). The operations team wants to improve the reliability and cost efficiency of the cluster. They observe that several workloads have no resource requests or limits set, and some nodes are consistently underutilised while others are overcommitted. Which two actions should the architect recommend to address these issues? (Choose two.)
⚠ Common exam trap
The trap here is thinking that simply adding more nodes or disabling autoscalers will fix utilisation, when the underlying issue is that pods lack the resource requests the scheduler needs.
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
✓
Configure resource requests and limits for all pods based on observed usage.
The core problems are inaccurate resource signalling and static node capacity. Defining requests and limits lets the scheduler place pods correctly and enables autoscaling features to work effectively. Enabling the cluster autoscaler then adjusts node pool size to match actual demand, adding capacity when pods are pending and removing idle nodes. Together they improve reliability during peaks and reduce cost during low usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable the horizontal pod autoscaler to avoid fluctuating replica counts.
Why it's wrong here
Disabling the horizontal pod autoscaler removes the ability to scale replicas based on load, which would hurt reliability during traffic spikes and waste resources during low demand. It does not fix the missing resource requests or the node imbalance, and it works against the goal of cost efficiency and reliability.
- ✗
Migrate all workloads to a single, larger node pool with no autoscaling.
Why it's wrong here
Consolidating into one fixed, large node pool does not address overcommitment or underutilisation because scheduling still depends on pod resource requests. Without autoscaling, the cluster cannot adapt to changing demand, so cost efficiency and reliability are both likely to suffer. It also reduces fault isolation across node pools.
- ✓
Configure resource requests and limits for all pods based on observed usage.
Why this is correct
Setting requests and limits gives the scheduler accurate information to place pods and prevents noisy-neighbour problems. It also enables the cluster autoscaler to make better scaling decisions and allows vertical pod autoscaling to right-size workloads. Without them, the scheduler cannot bin-pack efficiently, leading to the underutilisation and overcommitment described.
- ✓
Enable the cluster autoscaler on all node pools with appropriate minimum and maximum sizes.
Why this is correct
The cluster autoscaler adds nodes when pods are pending due to insufficient resources and removes underutilised nodes. Combined with accurate resource requests, it directly addresses the mix of idle and overcommitted nodes by scaling node pools to match demand. This improves both reliability during peaks and cost efficiency during lulls.
- ✗
Set the pod disruption budget for all deployments to zero.
Why it's wrong here
A pod disruption budget of zero would prevent voluntary disruptions entirely, which can block node drains and upgrades and reduce reliability rather than improve it. It does not address resource allocation or node utilisation, and it can cause maintenance operations to stall, making the cluster less resilient over time.
Go deeper
Related to this question
Learn chapter
Resource Monitoring and Logging with Cloud Operations
Key term
Anthos
Anthos is a Google Cloud platform that lets you run applications consistently across different computing environments, like on-premises data centers and multiple public clouds.
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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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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