Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
An application running on GKE Autopilot is experiencing intermittent failures due to resource limits. The team wants to ensure that the application always has enough CPU and memory without manual node management. What should they do?
⚠ Common exam trap
The trap is treating GKE Autopilot like GKE Standard — candidates pick 'create a larger node pool' or 'switch to Standard' because that is the Standard-mode answer, forgetting that Autopilot hides node management and the only correct lever is pod resource requests/limits.
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
✓
Increase the resource requests and limits in the pod specification
In GKE Autopilot, nodes are fully managed by Google, so the team cannot create or resize node pools. The correct lever is to set appropriate CPU and memory requests (and limits) in the pod specification so the scheduler and Autopilot's autoscaler provision nodes with sufficient capacity. Autopilot uses the requests to bin-pack pods and to decide when to add nodes, so undersized requests cause intermittent failures under load.
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 horizontal pod autoscaling only
Why it's wrong here
Horizontal pod autoscaling only adjusts replica counts against CPU or custom metrics; it cannot raise the CPU and memory requests that Autopilot uses to size pods, so limits still bite. It is tempting because HPA is the standard scaling tool, and would fit a traffic-driven load scenario.
- ✓
Increase the resource requests and limits in the pod specification
Why this is correct
Raising resource requests and limits in the pod specification directly addresses the intermittent failures by guaranteeing the scheduler reserves sufficient CPU and memory for each pod. On GKE Autopilot, nodes are provisioned automatically to match those requests, so adequate values remove throttling and OOM evictions without any manual node management.
- ✗
Create a new node pool with larger machine types
Why it's wrong here
Autopilot manages nodes itself and does not expose node pools for creation, so this is not available; pod resource requests drive node provisioning instead. It is tempting because larger machine types do add capacity, and would be correct on GKE Standard where node pools are user-managed.
- ✗
Switch to GKE Standard and manage node pools manually
Why it's wrong here
Moving to GKE Standard reintroduces the manual node pool management the team explicitly wants to avoid, and Autopilot already provisions nodes automatically. It is tempting because Standard grants full control over machine types and sizing, and would suit workloads needing custom node configurations or daemons.
Go deeper
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Key term
GKE Autopilot
GKE Autopilot is a managed mode of Google Kubernetes Engine that automatically handles node provisioning, scaling, and maintenance so you only pay for your running pods.
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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JA
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
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.