Accelerated computing EC2 instance is intended to handle workloads requiring GPUs or specialized hardware acceleration because accelerated instance families provide processors suited to parallel graphics or ML workloads; in this design the new outcome is to run Kubernetes-based container workloads using an AWS-managed control plane, which calls for Amazon EKS rather than Accelerated computing EC2 instance or the earlier AWS Fargate.

AWS Fargate primarily supports the earlier need to run container tasks without managing EC2 container hosts because fargate supplies serverless compute for supported ECS and EKS workloads, but the new task is to run Kubernetes-based container workloads using an AWS-managed control plane; that is why Amazon EKS is preferable here.

Amazon ECR is intended to store and distribute container images for deployment because eCR is a managed container image registry integrated with AWS deployment tooling; in this design the new outcome is to run Kubernetes-based container workloads using an AWS-managed control plane, which calls for Amazon EKS rather than Amazon ECR or the earlier AWS Fargate.

AWS Fargate primarily supports the earlier need to run container tasks without managing EC2 container hosts because fargate supplies serverless compute for supported ECS and EKS workloads, but the new task is to obtain eligible EC2 billing discounts for committed usage attributes; that is why Reserved Instances is preferable here.

EC2 Auto Scaling is intended to adjust the number of matching EC2 instances as demand changes because EC2 Auto Scaling can launch and terminate instances according to configured scaling policies; in this design the new outcome is to obtain eligible EC2 billing discounts for committed usage attributes, which calls for Reserved Instances rather than EC2 Auto Scaling or the earlier AWS Fargate.

Elastic Load Balancing is intended to spread inbound traffic across multiple eligible targets because eLB directs client traffic toward configured healthy application targets; in this design the new outcome is to obtain eligible EC2 billing discounts for committed usage attributes, which calls for Reserved Instances rather than Elastic Load Balancing or the earlier AWS Fargate.

Gateway Load Balancer is intended to insert compatible virtual network appliances into traffic paths because gateway Load Balancer distributes flows across appliance targets for inspection or similar tasks; in this design the new outcome is to operate virtual servers with explicit OS and instance management, which calls for Amazon EC2 rather than Gateway Load Balancer or the earlier Vertical EC2 scaling.

Vertical EC2 scaling primarily supports the earlier need to increase resource capacity by changing the instance type because vertical scaling provides more CPU or RAM within a single virtual machine, but the new task is to operate virtual servers with explicit OS and instance management; that is why Amazon EC2 is preferable here.

Horizontal EC2 scaling is intended to add parallel instance capacity for a distributed application because horizontal scaling adds multiple instances that can share workload when architecture supports it; in this design the new outcome is to operate virtual servers with explicit OS and instance management, which calls for Amazon EC2 rather than Horizontal EC2 scaling or the earlier Vertical EC2 scaling.

Compute optimized EC2 instance addresses a different objective, to provide comparatively more CPU resources for CPU-intensive applications, because compute-optimized instance families emphasize processing power for compute-bound tasks; the checklist instead requires the distinct outcomes to deploy supported web applications or container services without operating servers; increase resource capacity by changing the instance type.

EC2 Auto Scaling addresses a different objective, to adjust the number of matching EC2 instances as demand changes, because EC2 Auto Scaling can launch and terminate instances according to configured scaling policies; the checklist instead requires the distinct outcomes to deploy supported web applications or container services without operating servers; increase resource capacity by changing the instance type.

Spot interruption handling addresses a different objective, to design flexible EC2 workloads to withstand capacity reclamation, because Spot capacity may be interrupted by AWS, so tasks should checkpoint or retry; the checklist instead requires the distinct outcomes to deploy supported web applications or container services without operating servers; increase resource capacity by changing the instance type.

It does not replace the container orchestrator used to define and schedule tasks; that caveat is associated more closely with AWS Fargate for the need to run container tasks without managing EC2 container hosts than with EC2 Auto Scaling for the requirement to adjust the number of matching EC2 instances as demand changes.

An image does not itself continuously scale a production fleet; that caveat is associated more closely with Amazon EC2 AMI for the need to relaunch instances from a defined system image than with EC2 Auto Scaling for the requirement to adjust the number of matching EC2 instances as demand changes.

It is unsuitable for jobs that cannot tolerate an interruption; that caveat is associated more closely with Spot interruption handling for the need to design flexible EC2 workloads to withstand capacity reclamation than with EC2 Auto Scaling for the requirement to adjust the number of matching EC2 instances as demand changes.

AWS Batch is intended to queue and execute batch computing jobs with managed scheduling because batch provisions and schedules compute capacity for job queues and compute environments; in this design the new outcome is to execute event-triggered code without managing virtual servers, which calls for AWS Lambda rather than AWS Batch or the earlier AWS Fargate.

AWS Fargate primarily supports the earlier need to run container tasks without managing EC2 container hosts because fargate supplies serverless compute for supported ECS and EKS workloads, but the new task is to execute event-triggered code without managing virtual servers; that is why AWS Lambda is preferable here.

Gateway Load Balancer is intended to insert compatible virtual network appliances into traffic paths because gateway Load Balancer distributes flows across appliance targets for inspection or similar tasks; in this design the new outcome is to execute event-triggered code without managing virtual servers, which calls for AWS Lambda rather than Gateway Load Balancer or the earlier AWS Fargate.

That description belongs to Memory optimized EC2 instance because memory-optimized families provide higher RAM-to-vCPU ratios for memory-bound applications; it is used to support applications with large in-memory working sets, not to explain how Compute optimized EC2 instance provides the function to provide comparatively more CPU resources for CPU-intensive applications.

That description belongs to Amazon ECS because eCS schedules and manages containers in clusters on supported launch types; it is used to orchestrate containerized applications using AWS-managed control capabilities, not to explain how Compute optimized EC2 instance provides the function to provide comparatively more CPU resources for CPU-intensive applications.

That description belongs to AWS Elastic Beanstalk because elastic Beanstalk simplifies web-app deployment and orchestrates the supporting AWS components; it is used to deploy supported applications while the platform provisions common resources, not to explain how Compute optimized EC2 instance provides the function to provide comparatively more CPU resources for CPU-intensive applications.

AWS Solutions Architects is intended to discuss architecture choices and best-practice approaches with AWS technical specialists because AWS solutions architects advise on designs that align with workload needs; in this design the new outcome is to study architecture and security guidance published by AWS, which calls for AWS whitepapers rather than AWS Solutions Architects or the earlier AWS Security Blog.

AWS Enterprise Support is intended to receive deeper proactive expert guidance for critical organization workloads because enterprise Support targets business-critical customers requiring advanced guidance and support engagement; in this design the new outcome is to study architecture and security guidance published by AWS, which calls for AWS whitepapers rather than AWS Enterprise Support or the earlier AWS Security Blog.

AWS Security Blog primarily supports the earlier need to learn about AWS security guidance and announcements because the Security Blog publishes security-related service explanations and practices, but the new task is to study architecture and security guidance published by AWS; that is why AWS whitepapers is preferable here.

That description belongs to EC2 compute versus EBS charges because EBS capacity and eligible provisioned performance can accrue charges independent of EC2 running state; it is used to account for storage bills separately from instance runtime, not to explain how Serverless invocation pricing provides the function to align charges with events and execution rather than idle VM runtime.

That description belongs to Cross-Region data transfer because data transferred between Regions can incur regional transfer charges; it is used to account for charges when moving data between AWS Regions, not to explain how Serverless invocation pricing provides the function to align charges with events and execution rather than idle VM runtime.

That description belongs to Data transfer out to internet because outbound traffic may generate data-transfer charges depending on service and free allowances; it is used to estimate egress charges for delivering AWS data to external users, not to explain how Serverless invocation pricing provides the function to align charges with events and execution rather than idle VM runtime.

That description belongs to AWS Cost Optimization Hub because cost Optimization Hub provides a consolidated view of eligible recommendations; it is used to review aggregated potential cost-saving opportunities, not to explain how Cost Explorer rightsizing recommendations provides the function to identify potential savings from overprovisioned compute.

That description belongs to Savings Plans recommendations because cost management recommendations analyze past usage for commitment opportunities; it is used to estimate potential discounts from stable eligible compute spend, not to explain how Cost Explorer rightsizing recommendations provides the function to identify potential savings from overprovisioned compute.

That description belongs to Cost anomaly detection because AWS Cost Anomaly Detection monitors supported cost signals and can alert on anomalies; it is used to identify unusually high spending patterns that depart from expectations, not to explain how Cost Explorer rightsizing recommendations provides the function to identify potential savings from overprovisioned compute.

Pay-as-you-go consumption addresses the separate need to replace much upfront infrastructure spending with metered service usage because AWS prices many resources based on usage duration volume requests or capacity; it is not as direct as Cross-Region data transfer for the actual requirement to account for charges when moving data between AWS Regions.

Serverless invocation pricing addresses the separate need to align charges with events and execution rather than idle VM runtime because services such as Lambda charge based on request and execution characteristics under their pricing models; it is not as direct as Cross-Region data transfer for the actual requirement to account for charges when moving data between AWS Regions.

Storage request pricing addresses the separate need to include operations alongside capacity when estimating object-storage spend because object storage can charge for API requests in addition to stored gigabytes; it is not as direct as Cross-Region data transfer for the actual requirement to account for charges when moving data between AWS Regions.