Google PCA Manage implementation of cloud architecture Practice Question
What are two best practices for designing a scalable Kubernetes architecture on GKE?
⚠ Common exam trap
Google Cloud often tests the misconception that StatefulSets are interchangeable with Deployments for stateless apps, or that disabling Cluster Autoscaler simplifies management, but the trap here is that candidates may overlook the need for multi-zonal clusters and autoscaling mechanisms to achieve true scalability and resilience in GKE.
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
✓
Enable horizontal pod autoscaling
Option C is correct because enabling Horizontal Pod Autoscaling (HPA) lets GKE automatically adjust the number of pod replicas based on metrics such as CPU utilization or custom metrics, which is essential for handling variable load in a scalable architecture. Option D is correct because using multiple node pools with different machine types allows you to right-size workloads, isolate resource-intensive or specialized workloads (e.g., GPU, memory-optimized), and scale each pool independently, improving both efficiency and scalability. Option A is incorrect because StatefulSets are designed for stateful applications requiring stable network identities and persistent storage, not stateless workloads, which are better served by Deployments. Option B is incorrect because disabling the Cluster Autoscaler prevents nodes from being added or removed automatically as demand changes, undermining scalability. Option E is incorrect because a single-zone cluster concentrates resources in one zone, reducing availability and limiting the ability to scale resiliently across zones.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use StatefulSets for stateless applications
Why it's wrong here
StatefulSets give each pod a stable identity, ordinal name and persistent volume claim, which stateless workloads do not need and which complicates rolling updates and scaling. Deployments suit stateless applications. StatefulSets are correct for databases, queues or anything requiring stable network identity and per-pod storage.
- ✗
Disable Cluster Autoscaler
Why it's wrong here
Disabling Cluster Autoscaler prevents node pools from adding nodes when pods remain Pending due to insufficient CPU or memory, so capacity cannot follow demand. It suits fixed-capacity environments with strict budget or licensing limits. Autoscaling is required for elastic, demand-driven scaling.
- ✓
Enable horizontal pod autoscaling
Why this is correct
Horizontal pod autoscaling adjusts replica counts dynamically based on observed CPU, memory or custom metrics, so the cluster absorbs traffic spikes without manual intervention. This directly satisfies the scalability requirement by matching capacity to demand, preventing both resource starvation under load and idle waste during quiet periods.
- ✓
Use node pools with different machine types
Why this is correct
Node pools let you match machine families and sizes to workload profiles, so CPU-heavy and memory-heavy pods land on appropriate hardware. This satisfies the scalability constraint by enabling independent cluster autoscaler scaling per pool rather than forcing one machine type across all workloads.
- ✗
Use a single zone cluster
Why it's wrong here
A single-zone cluster concentrates all nodes in one failure domain, so a zone outage takes down every workload and the control plane cannot reschedule pods elsewhere. Multi-zonal node pools spread replicas across zones. Single-zone suits dev/test or latency-sensitive workloads where cross-zone traffic cost matters.
Go deeper
Related to this question
Learn chapter
Google Kubernetes Engine (GKE)
Key term
Scalability
Scalability is the ability of a system, network, or process to handle a growing amount of work by adding resources, either by making the existing resources more powerful (vertical scaling) or by adding more resources (horizontal scaling).
Key term
Autoscaler
An Autoscaler is a cloud service that automatically increases or decreases the number of virtual machines (instances) or resources based on real-time demand, so your application always has enough capacity without wasting money on idle servers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PCA 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 PCA exam.