The correct answer is: Implement a target tracking policy in an EC2 Auto Scaling group using the ALB and its existing target group. Set the minimum number of instances to 2, desired to 3, and maximum to 6. Use the average CPU utilization metric and aim for a 50% target threshold.

This solution leverages Amazon EC2 Auto Scaling with a target tracking policy based on CPU utilization. Target tracking allows the infrastructure to adjust automatically when CPU metrics surpass or fall below the specified targetin this case, 50%. This setup optimizes cost during low-traffic periods by scaling down, while also ensuring resource availability during high-traffic periods by scaling up. The use of the Application Load Balancer ensures requests are evenly distributed across healthy instances.

The option Set up two CloudWatch alarms: one for low CPU usage and one for high CPU usage. Connect the alarms to an SNS topic that emails the administrator, who can then manually log in to scale instances as needed is not practical. Manual intervention introduces delays and undermines the goal of automated scalability. This approach is reactive and labor-intensive, which defeats the benefits of cloud elasticity.

The option Create a CloudWatch alarm that triggers when CPU drops below 25%, and have it invoke a Lambda function that shuts down one of the EC2 instances from the load balancers target group is risky. It assumes that low CPU means over-provisioning, which might not always be the case. Additionally, terminating instances arbitrarily based on single metrics without proper orchestration can lead to instability and availability issues.

The option Establish an EC2 Auto Scaling group linked to the current ALB and its target group. Configure the group with 2 minimum instances, 3 desired, and 6 maximum instances, but do not set any dynamic scaling policy provides a basic scaling structure but fails to automate changes based on workload fluctuations. Without a scaling policy, it will not adjust the instance count as demand changes, which compromises both cost-efficiency and responsiveness.

This question is important because it tests your understanding of how to implement dynamic scaling strategies that respond to real-world workload patterns. It also checks your knowledge of integrating Auto Scaling groups with ALBs and CloudWatch metrics. A good trick for questions like this is to look for automated and dynamic solutions over manual or reactive ones. Watch out for distractors that sound technical but don't fulfill the requirements for automation or cost optimization.

The challenge in this question lies in distinguishing between merely configuring infrastructure and actually enabling responsive automation. Its easy to be drawn to plausible-sounding setups that dont meet the real goal: scalable, cost-efficient automation aligned with workload demand.

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