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Answer: Opt in to AWS Compute Optimizer. Allow sufficient time for metrics to be gathered. Review the Compute Optimizer findings for EBS volumes.
AWS Compute Optimizer uses machine learning to analyze historical utilization metrics and generate recommendations to reduce costs and improve performance. It directly supports recommendations for EBS volumes (specifically evaluating IOPS and throughput) and generates insights automatically, which makes it much more operationally efficient than manual benchmarking or reviewing metrics instance-by-instance.
Author: Ritesh Yadav
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Question #18
A company has users that deploy Amazon EC2 instances that have more volume performance capacity than is required. A CloudOps engineer needs to review all Amazon Elastic Block Store (Amazon EBS) volumes that are associated with the instances and create cost optimization recommendations based on IOPS and throughput. What should the CloudOps engineer do to meet these requirements in the MOST operationally efficient way?
A
Use the monitoring graphs in the EC2 console to view metrics for EBS volumes. Review the consumed space against the provisioned space on each volume. Identify any volumes that have low utilization.
B
Stop the EC2 instances from the EC2 console. Change the EC2 instance type to Amazon EBS-optimized. Start the EC2 instances.
C
Opt in to AWS Compute Optimizer. Allow sufficient time for metrics to be gathered. Review the Compute Optimizer findings for EBS volumes.
D
Install the fio tool onto the EC2 instances and create a .cfg file to approximate the required workloads. Use the benchmark results to gauge whether the provisioned EBS volumes are of the most appropriate type.
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