CV0-002 · Question #31
A cloud engineer notices on a dashboard that the host is close to reaching maximum capacity for the CPU and memory in the cloud environment, which could cause performance issues. The cloud…
The correct answer is B. Adjust the cloud workload by migrating resource-intensive applications to different hosts. To optimize compute usage efficiency when a host is nearing capacity due to uneven workloads, the engineer should adjust the cloud workload by migrating resource-intensive applications to different, less utilized hosts.
Question
A cloud engineer notices on a dashboard that the host is close to reaching maximum capacity for the CPU and memory in the cloud environment, which could cause performance issues. The cloud environment has 100 servers, with 25% of the servers consuming their compute only during peak business hours, 25% consuming half of the allocated resources, and the remaining 50% using the compute during off hours. Which of the following should the engineer perform to optimize the efficiency of the compute usage in the cloud?
Options
- AAdd additional CPUs and RAM to the host that is serving the cloud.
- BAdjust the cloud workload by migrating resource-intensive applications to different hosts.
- CAdd additional hosts to the environment using the cloud management tool.
- DEnable automatic scaling in the cloud management tool.
How the community answered
(23 responses)- A4% (1)
- B83% (19)
- C4% (1)
- D9% (2)
Why each option
To optimize compute usage efficiency when a host is nearing capacity due to uneven workloads, the engineer should adjust the cloud workload by migrating resource-intensive applications to different, less utilized hosts.
Adding additional CPUs and RAM to the host increases capacity but does not address the underlying inefficiency of workload distribution across the existing environment.
The description indicates varying resource consumption patterns across servers. Migrating resource-intensive applications to hosts with available capacity or to hosts with complementary off-peak usage patterns directly rebalances the workload, improving overall efficiency and preventing a single host from reaching maximum capacity.
Adding additional hosts increases overall capacity but does not optimize the efficiency of the *existing* compute usage or address the uneven distribution described.
Enabling automatic scaling is beneficial for dynamically growing or shrinking workloads, but the problem focuses on optimizing static, existing workload distribution to improve efficiency, not on reacting to sudden demand changes.
Concept tested: Cloud workload optimization and resource management
Source: https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/manage/cost-management/optimize-compute
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