Amazon Web Services Updated SAA-C03 Exam Questions and Answers by ekam

 This option is the most efficient because it uses Amazon CloudFront, which is a web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users1. It also uses a CloudFront distribution for the static content, which reduces the load on the EC2 instances and improves the performance and availability of the website. It also uses an Auto Scaling group to launchnew instances based on network traffic, which automatically adjusts the compute capacity of your EC2 instances based on load or aschedule2. This solution meets the requirement of ensuring that all the requests are processed successfully during events for the launch of new products. Option A is less efficient because it uses a CloudFront distribution for the dynamic content, which is not necessary as the dynamic content is already handled by the API on the EC2 instances. It also increases the number of EC2 instances to handle the increase in traffic, which could incur higher costs and complexity than using an Auto Scaling group. Option C is less efficient because it uses an Amazon ElastiCache instance in front of the ALB to reduce traffic for the API to handle, which is a way to provide afully managed in-memory data store service that provides sub-millisecond latency for caching and data processing3. However, this could introduce additional complexity and latency, and does not scale automatically based on network traffic. Option D is less efficient because it uses an Amazon Simple Queue Service (Amazon SQS) queue to receive requests from the website for later processing by the EC2 instances, which is a way to send, store, and receive messages between software components at any volume. However, this does not provide a faster response time to the users as they have to wait for their requests to be processed by the EC2 instances.

• An Auto Scaling group will automatically scale the EC2 instances to match changes in demand. This optimizes cost by only running as many instances as needed.

• A target tracking scaling policy monitors the ASGAverageCPUUtilization metric and scales to keep the average CPU around the 50% target value. This ensures there are enough resources during CPU surges.

• The ALB and target group are reused, so the application architecture does not change. The Auto Scaling group is associated to the existing load balancer setup.

• A minimum of 2 and maximum of 6 instances provides the ability to scale between 3 and 6 instances as needed based on demand.

• Costs are optimized by starting with only 3 instances (the desired capacity) and scaling up as needed. When CPU usage drops, instances are terminated to match the desired capacity.

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

Restricting inbound access to the web servers to only port 443, which is used for HTTPS traffic, and allowing access from any IP address (0.0.0.0/0), since the application is public and accessible for global customers.

Restricting inbound access to the DB instance to only port 3306, which is used for MySQL traffic, and allowing access only from the security group of the web servers, which creates a secure connection between the two tiers and prevents unauthorized access to the database.

Restricting outbound access to the minimum required for both tiers, which is not specified in the question but can be assumed to be similar to the inbound rules.