Google Professional Cloud DevOps Engineer Question 160
Select 3Google Cloud PlatformYour team manages a web application hosted on Google Kubernetes Engine (GKE) that experiences intermittent high latency during peak traffic hours. After analyzing the system, you identify that the application is hitting resource limits and node scaling is delayed, leading to performance degradation. What are the MOST effective steps for addressing this issue?
- A
Enable Kubernetes Horizontal Pod Autoscaler (HPA) to dynamically adjust the number of pods based on resource usage.
- B
Configure GKE cluster autoscaler to scale nodes automatically based on pod resource requests.
- C
Increase the CPU and memory limits for the application pods to avoid throttling during peak traffic.
- D
Switch to using preemptible VMs for your GKE cluster to reduce costs.
- E
Set up Cloud Monitoring alerts to proactively notify you of resource saturation.
Show answer and explanation
Correct answers: A, B, E
Explanation
To address performance and latency issues during peak traffic, it is critical to implement solutions that ensure dynamic resource scaling. Enabling HPA allows pods to scale based on workload demands, while configuring the cluster autoscaler ensures sufficient nodes are available to support the pods. Monitoring resource saturation through Cloud Monitoring alerts provides proactive insights, allowing the team to respond swiftly to potential issues. Increasing resource limits and switching to preemptible VMs do not directly address the root cause of delayed scaling and may introduce other challenges.
- A. Correct.
Enabling Kubernetes Horizontal Pod Autoscaler (HPA) allows the system to dynamically scale the number of pods based on CPU, memory, or custom metrics, helping to handle traffic spikes effectively.
- B. Correct.
Configuring the GKE cluster autoscaler ensures that the nodes in the cluster can scale up or down based on the resource needs of the pods, addressing delayed node scaling issues.
- C. Incorrect.
While increasing CPU and memory limits might temporarily alleviate resource contention, it does not address the underlying issue of dynamic scaling for fluctuating traffic patterns.
- D. Incorrect.
Using preemptible VMs can reduce costs but introduces the risk of workloads being interrupted, which is not suitable for addressing latency issues during peak traffic.
- E. Correct.
Setting up Cloud Monitoring alerts helps detect resource saturation early and enables proactive responses, but it does not directly resolve the scaling issue.