CV0-004 Operations and Support Practice Question
A cloud administrator is deploying a containerized workload to Google Kubernetes Engine. The workload must automatically scale based on the number of incoming HTTP requests per second rather than CPU utilization. Which GKE feature should the administrator configure to meet this requirement?
⚠ Common exam trap
The trap here is assuming the Cluster Autoscaler scales application replicas, when it only adjusts the underlying node count in response to scheduling pressure.
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
✓
Horizontal Pod Autoscaler with a custom metric from Cloud Monitoring
The Horizontal Pod Autoscaler is the GKE mechanism that changes replica counts in response to metrics. By supplying a custom metric sourced from Cloud Monitoring that represents HTTP requests per second, the administrator can scale on request rate rather than CPU, which is exactly the workload signal described.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
GKE Autopilot mode with burst scaling enabled
Why it's wrong here
Autopilot mode manages node provisioning and applies opinionated defaults, but it does not provide request-rate-based pod scaling. Burst scaling is not a GKE feature for scaling replicas on HTTP throughput. Autopilot still relies on the Horizontal Pod Autoscaler for replica scaling decisions, so it does not satisfy the custom metric requirement.
- ✗
Vertical Pod Autoscaler in recommendation mode
Why it's wrong here
The Vertical Pod Autoscaler adjusts CPU and memory requests and limits for existing pods rather than changing replica counts. It is not designed to react to request-rate signals, and recommendation mode only suggests values without applying them. It therefore cannot scale the workload in response to HTTP request throughput.
- ✗
Cluster Autoscaler with node pool autoscaling enabled
Why it's wrong here
The Cluster Autoscaler adds or removes nodes when pods cannot be scheduled or nodes are underutilized. It responds to scheduling pressure, not to application request rates. While it complements pod autoscaling, it does not itself scale the number of application replicas based on HTTP requests per second.
- ✓
Horizontal Pod Autoscaler with a custom metric from Cloud Monitoring
Why this is correct
The Horizontal Pod Autoscaler supports autoscaling based on custom and external metrics, not just CPU or memory. By exporting requests-per-second to Cloud Monitoring and referencing it as a custom metric in the HPA specification, the administrator can scale pods directly on HTTP request rate, which matches the stated requirement.
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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 CompTIA exam blueprint
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