A company plans to migrate 1,000 VMware virtual machines to AWS. As part of planning, they want to collect server-level metrics (CPU details, RAM usage, OS information, running processes) from on-premises hosts, then query and analyze that data. Which solution satisfies these requirements?

The AWS Application Discovery Agent collects detailed server-level metrics, including CPU, RAM, OS, and running processes, directly from each on-premises server. This data is then sent to AWS Migration Hub. Enabling Data Exploration in Migration Hub stores this collected data in an Amazon S3 bucket, which can then be queried efficiently using Amazon Athena. This provides a scalable and cost-effective solution for analyzing the collected information. The Agentless Discovery Connector appliance primarily focuses on discovering servers and their dependencies, not collecting detailed OS-level metrics like running processes. Exporting only VM performance metrics and manually updating attributes is inefficient and prone to errors for 1,000 VMs. Using AWS CLI put-resource-attributes is for metadata, not for continuous collection of detailed server metrics, and the Migration Hub console has limited querying capabilities compared to Athena.