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NEW QUESTION: 1
You have a large number of web servers in an Auto Scalinggroup behind a load balancer. On an hourly basis,
you want to filter and process the logs to collect data on unique visitors, and then put that data in a durable
data store in order to run reports. Web servers in the Auto Scalinggroup are constantly launching and
terminating based on your scaling policies, but you do not want to lose any of the log data from these servers
during a stop/termination initiated by a user or by Auto Scaling. What two approaches will meet these
requirements? Choose two answers from the optionsgiven below.
A. On the web servers, create a scheduled task that executes a script to rotate and transmit the logs to
Amazon Glacier. Ensure that the operating system shutdown procedure triggers a logs transmission
when the Amazon EC2 instance is stopped/terminated. Use Amazon Data Pipeline to process the data in
Amazon Glacier and run reports every hour.
B. Install an AWS Data Pipeline Logs Agent on every web server during the bootstrap process. Create a
log group object in AWS Data Pipeline, and define Metric Filters to move processed log data directly
from the web servers to Amazon Redshift and run reports every hour.
C. On the web servers, create a scheduled task that executes a script to rotate and transmit the logs to an
Amazon S3 bucket. Ensure that the operating system shutdown procedure triggers a logs transmission
when the Amazon EC2 instance is stopped/terminated. Use AWS Data Pipeline to move log data from
the Amazon S3 bucket to Amazon Redshift In order to process and run reports every hour.
D. Install an Amazon Cloudwatch Logs Agent on every web server during the bootstrap process. Create a
CloudWatch log group and define
Metric Filters to create custom metrics that track unique visitors from the streaming web server logs.
Create a scheduled task on an Amazon EC2 instance that runs every hour to generate a new report based
on the Cloudwatch custom metrics.