SOA-C02 Exam Dumps : AWS Certified SysOps Administrator - Associate (SOA-C02)

To view the cost details of each project in an AWS account using Cost Explorer, you need to activate cost allocation tags and tag the resources with a project identifier.

Activate Cost Allocation Tags:

Navigate to the AWS Billing and Cost Management console.

In the navigation pane, choose "Cost allocation tags".

Select the tags you want to activate (e.g., "Project") and click "Activate".

Tag Resources with Project Identifier:

Go to the AWS Management Console for each service where resources are deployed (e.g., EC2, S3, RDS).

For each resource, add a tag with the key "Project" and a value that identifies the project (e.g., "ProjectA").

Ensure that all relevant resources are tagged appropriately.

View Cost Allocation in Cost Explorer:

Navigate to the Cost Explorer console.

Use the "Group by" option to group costs by the "Project" tag.

Review the cost details for each project based on the tags applied to the resources.

Using Cost Allocation Tags

Analyzing Your Costs with Cost Explorer

"Network Load Balancer is optimized to handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone."

For an application that must scale to millions of requests per second and requires a single static IP address for each Availability Zone, a Network Load Balancer (NLB) is the most suitable option. NLBs are designed for high-performance, low-latency networking, and they support static IP addresses for each Availability Zone, making it ideal for volatile traffic patterns. Option D is the correct choice. AWS provides extensive documentation on NLB capabilities and configurations that suit these requirements AWS Network Load Balancer.

Increasing the amount of memory for the Lambda function will help to improve the performance of the function. This is because the Lambda function is CPU-intensive and increasing the memory will give it access to more CPU resources and help it run faster. The other options (activating hyperthreading in the CPU launch options for the Lambda function, turning off the AWS managed encryption, and loading the required code into a custom layer) will not help to improve the performance of the Lambda function and are not the correct solutions for this issue.