Google Professional Cloud Developer Question 383
Select 3Google Cloud PlatformYou are tasked with deploying a new containerized workload on Google Kubernetes Engine (GKE). The workload is CPU-intensive and requires high memory to function properly. Additionally, you want to ensure that the workload can scale efficiently under increased demand. How should you define the workload specifications to meet these requirements?
- A
Set resource requests and limits for both CPU and memory in the Pod specification.
- B
Use a Horizontal Pod Autoscaler (HPA) to scale the deployment based on CPU utilization.
- C
Deploy the workload on a node pool with preemptible VM instances to save costs.
- D
Choose machine types with sufficient CPU and memory in the node pool to match workload demands.
- E
Rely only on default resource settings provided by Kubernetes for simplicity.
Show answer and explanation
Correct answers: A, B, D
Explanation
To ensure the workload performs optimally and scales efficiently, you need to define resource requests and limits to guarantee resource allocation, use a Horizontal Pod Autoscaler for dynamic scaling, and select node types with sufficient resources to match the workload's demands. Default settings or preemptible VMs are not suitable for a critical, resource-intensive workload.
- A. Correct.
Correct. Setting resource requests and limits ensures the workload gets the necessary resources and prevents other Pods from over-consuming resources on the same node.
- B. Correct.
Correct. A Horizontal Pod Autoscaler allows the workload to scale horizontally based on metrics like CPU utilization, which is critical for handling increased demand.
- C. Incorrect.
Incorrect. While preemptible VMs are cost-effective, they are not suitable for workloads requiring high availability or consistent resource guarantees.
- D. Correct.
Correct. Choosing machine types with sufficient CPU and memory ensures that the nodes can handle the resource-intensive workload without bottlenecks.
- E. Incorrect.
Incorrect. Default resource settings in Kubernetes may not meet the specific demands of CPU-intensive and memory-intensive workloads, leading to potential instability.