Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
A media company runs a video transcoding service on GKE Standard. The service experiences sudden traffic spikes, and the operations team wants to ensure that the cluster can scale nodes automatically and that pods are rescheduled quickly when a node fails. The team also wants to monitor and alert on resource saturation. Which two actions should the cloud architect take to meet these requirements? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse pod-level scaling with node-level scaling, assuming that a HorizontalPodAutoscaler alone will add capacity when in fact cluster autoscaler is what provisions new nodes.
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 cluster autoscaler on the node pools and set appropriate minimum and maximum node counts based on expected peak load.
Automatic node scaling requires cluster autoscaler on the node pools, and it works correctly only when pods declare accurate resource requests and limits so the scheduler and autoscaler can size capacity. Together these ensure nodes are added during spikes and pods are placed and rescheduled efficiently. Pod disruption budgets, Cloud CDN, and HorizontalPodAutoscaler address different concerns and do not provide node-level scaling or rapid recovery from node failure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable cluster autoscaler on the node pools and set appropriate minimum and maximum node counts based on expected peak load.
Why this is correct
Cluster autoscaler adjusts the number of nodes in a node pool when pods cannot be scheduled due to insufficient resources, and it removes underutilized nodes when demand falls. Setting minimum and maximum counts bounds cost and capacity. This directly addresses automatic node scaling during traffic spikes and is a core reliability control for GKE workloads.
- ✗
Create a HorizontalPodAutoscaler based on CPU utilization for the transcoding deployment to add more pods when demand increases.
Why it's wrong here
A HorizontalPodAutoscaler adds or removes pods based on metrics like CPU utilization, which is valuable for handling load, but it does not add nodes. Without cluster autoscaler, new pods can remain pending if nodes lack capacity. The question asks for automatic node scaling and rapid rescheduling after node failure, so pod-level scaling alone is insufficient and does not meet the requirement.
- ✗
Enable Cloud CDN in front of the transcoding service to cache video segments and reduce load on the GKE pods.
Why it's wrong here
Cloud CDN caches HTTP responses at edge locations, which helps for cacheable content delivery but does not help with compute-intensive transcoding jobs that generate unique outputs. It also does not influence node autoscaling or pod rescheduling. While caching could reduce some read traffic, it does not satisfy the requirements for automatic node scaling and rapid pod recovery after node failure.
- ✗
Configure pod disruption budgets for the transcoding deployment to guarantee a minimum number of available pods during voluntary disruptions.
Why it's wrong here
Pod disruption budgets control voluntary disruptions such as node drains during upgrades or autoscaler scale-down, ensuring a minimum number of pods stay available. They do not trigger node scaling during traffic spikes, nor do they cause rapid rescheduling after an involuntary node failure. This action is useful for availability during maintenance but does not meet the stated scaling and failure-rescheduling goals.
- ✓
Set resource requests and limits on the transcoding pods so the scheduler and autoscaler can make accurate decisions about capacity and placement.
Why this is correct
Accurate resource requests let the Kubernetes scheduler place pods correctly and let cluster autoscaler determine when additional nodes are needed. Limits prevent a single pod from consuming all node resources. Without proper requests and limits, autoscaling decisions become unreliable and pods may be evicted or starved, so this is essential for predictable scaling and rapid rescheduling.
Go deeper
Related to this question
Learn chapter
Resource Monitoring and Logging with Cloud Operations
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.
Key term
GKE Standard
GKE Standard is a Google Kubernetes Engine cluster mode where you pay for the underlying virtual machines and manage the cluster infrastructure yourself, including node pools and scaling decisions.
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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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