An application has microservices spread across different AWS accounts and is integrated with an on-premises legacy system for some of its functionality. Because...

An application has microservices spread across different AWS accounts and is integrated with an on-premises legacy system for some of its functionality.
Because of the segmented architecture and missing logs, every time the application experiences issues, it is taking too long to gather the logs to identify the issues. A DevOps Engineer must fix the log aggregation process and provide a way to centrally analyze the logs.
Which is the MOST efficient and cost-effective solution?

A. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to export on-premises logs, and store the logs in an S3 bucket in a central account. Build an Amazon EMR cluster to reduce the logs and derive the root cause. B. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to import on-premises logs. Store all logs in S3 buckets in individual accounts. Use Amazon Macie to write a query to search for the required specific event-related data point. C. Collect system logs and application logs using the Amazon CloudWatch Logs agent. Install the CloudWatch Logs agent on the on-premises servers. Transfer all logs from AWS to the on-premises data center. Use an Amazon Elasticsearch Logstash Kibana stack to analyze logs on premises. D. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Install a CloudWatch Logs agent for on-premises resources. Store all logs in an S3 bucket in a central account. Set up an Amazon S3 trigger and an AWS Lambda function to analyze incoming logs and automatically identify anomalies. Use Amazon Athena to run ad hoc queries on the logs in the central account.