Horizontal scaling is intended to spread work across additional independent resources because horizontal scaling increases the number of workers or instances serving a workload; in this design the new outcome is to match provisioned resources to measured workload requirements, which calls for Right-sized architecture rather than Horizontal scaling or the earlier Resource-efficient software.

Resource-efficient software primarily supports the earlier need to avoid unnecessary processing and storage when delivering the same output because reducing wasted computation contributes to performance and sustainability objectives, but the new task is to match provisioned resources to measured workload requirements; that is why Right-sized architecture is preferable here.

Experiment-driven performance choices is intended to benchmark workload alternatives before standardizing a configuration because measurement of actual application behavior supports appropriate performance trade-offs; in this design the new outcome is to match provisioned resources to measured workload requirements, which calls for Right-sized architecture rather than Experiment-driven performance choices or the earlier Resource-efficient software.

Capital expenditure (CapEx) is intended to recognize upfront infrastructure purchase as a fixed investment because capEx involves purchasing owned assets such as servers and data-center equipment before the benefits are consumed; in this design the new outcome is to attribute cloud spending to teams and projects, which calls for Resource tagging for accountability rather than Capital expenditure (CapEx) or the earlier Cost visibility.

Savings Plans commitment is intended to obtain eligible compute discounts in exchange for a spending commitment because savings Plans provide discounts for qualifying usage during a commitment term; in this design the new outcome is to attribute cloud spending to teams and projects, which calls for Resource tagging for accountability rather than Savings Plans commitment or the earlier Cost visibility.

Cost visibility primarily supports the earlier need to understand resource expenses before changing deployment decisions because cost reporting and allocation tools can reveal spend drivers that are less apparent in a shared data center, but the new task is to attribute cloud spending to teams and projects; that is why Resource tagging for accountability is preferable here.

AWS CAF Security perspective primarily supports the earlier need to embed protection, risk practices and security capabilities into migration because the Security perspective helps establish security governance and resilient protective controls, but the new task is to modify an application to exploit cloud-native capabilities more substantially; that is why Refactor or re-architect is preferable here.

Rehost (lift and shift) is intended to move an application to cloud infrastructure with minimal redesign because rehosting relocates a workload while largely preserving its existing architecture; in this design the new outcome is to modify an application to exploit cloud-native capabilities more substantially, which calls for Refactor or re-architect rather than Rehost (lift and shift) or the earlier AWS CAF Security perspective.

Retire is intended to remove an application that no longer delivers sufficient business value because retirement eliminates unneeded systems from the future estate; in this design the new outcome is to modify an application to exploit cloud-native capabilities more substantially, which calls for Refactor or re-architect rather than Retire or the earlier AWS CAF Security perspective.

Capital expenditure (CapEx) is intended to recognize upfront infrastructure purchase as a fixed investment because capEx involves purchasing owned assets such as servers and data-center equipment before the benefits are consumed; in this design the new outcome is to attribute cloud spending to teams and projects, which calls for Resource tagging for accountability rather than Capital expenditure (CapEx) or the earlier Return on investment (ROI).

Savings Plans commitment is intended to obtain eligible compute discounts in exchange for a spending commitment because savings Plans provide discounts for qualifying usage during a commitment term; in this design the new outcome is to attribute cloud spending to teams and projects, which calls for Resource tagging for accountability rather than Savings Plans commitment or the earlier Return on investment (ROI).

Return on investment (ROI) primarily supports the earlier need to evaluate expected financial benefit relative to an investment because rOI compares gains with associated investment costs for a business decision, but the new task is to attribute cloud spending to teams and projects; that is why Resource tagging for accountability is preferable here.

That description belongs to AWS Schema Conversion Tool (AWS SCT) because AWS SCT helps transform schema objects and certain database code between supported engines; it is used to convert database schema and code for a heterogeneous database migration, not to explain how Replatform provides the function to make limited optimizations during migration without fully redesigning the application.

That description belongs to AWS Migration Hub because migration Hub helps provide visibility across a portfolio's migration activity; it is used to track migration progress across applications and migration tools, not to explain how Replatform provides the function to make limited optimizations during migration without fully redesigning the application.

That description belongs to Rehost (lift and shift) because rehosting relocates a workload while largely preserving its existing architecture; it is used to move an application to cloud infrastructure with minimal redesign, not to explain how Replatform provides the function to make limited optimizations during migration without fully redesigning the application.

That description belongs to Fault tolerance because fault-tolerant systems absorb failures using redundancy, isolation and suitable application behavior; it is used to continue operating despite an individual infrastructure component failing, not to explain how Reduced undifferentiated heavy lifting provides the function to spend less staff effort maintaining generic infrastructure components.

That description belongs to Experimentation at low initial commitment because usage-based provisioning enables limited experiments that can be shut down if unsuccessful; it is used to test a prototype without large upfront infrastructure investment, not to explain how Reduced undifferentiated heavy lifting provides the function to spend less staff effort maintaining generic infrastructure components.

That description belongs to Faster time to market because on-demand delivery of infrastructure helps shorten build and launch cycles; it is used to release a customer-facing capability with less infrastructure lead time, not to explain how Reduced undifferentiated heavy lifting provides the function to spend less staff effort maintaining generic infrastructure components.

Reliability pillar addresses a different objective, to help a workload recover from failures and continue functioning correctly, because reliability covers recovery, capacity planning and resilience to infrastructure or service disruption; the checklist instead requires the distinct outcomes to apply controlled repeatable procedures to infrastructure and operations; allow application components to fail or scale with less direct dependency on each other.

Performance efficiency pillar addresses a different objective, to select and adapt resources to meet performance needs efficiently, because performance efficiency includes choosing suitable technologies and maintaining efficient resource use as demand changes; the checklist instead requires the distinct outcomes to apply controlled repeatable procedures to infrastructure and operations; allow application components to fail or scale with less direct dependency on each other.

Automate recovery addresses a different objective, to restore normal application behavior without waiting for routine manual intervention, because automated detection and recovery reduce the time needed to handle predictable failure conditions; the checklist instead requires the distinct outcomes to apply controlled repeatable procedures to infrastructure and operations; allow application components to fail or scale with less direct dependency on each other.

Savings Plans commitment primarily supports the earlier need to obtain eligible compute discounts in exchange for a spending commitment because savings Plans provide discounts for qualifying usage during a commitment term, but the new task is to treat recurring service consumption as a running operational cost; that is why Operating expenditure (OpEx) is preferable here.

Variable cloud costs is intended to align consumption-based expense with actual usage because usage-based pricing lets spending change with resource consumption instead of requiring fixed purchased capacity; in this design the new outcome is to treat recurring service consumption as a running operational cost, which calls for Operating expenditure (OpEx) rather than Variable cloud costs or the earlier Savings Plans commitment.

Automation of shutdown schedules is intended to avoid running nonproduction compute unnecessarily because scheduled automation can stop or remove eligible idle resources when they are not required; in this design the new outcome is to treat recurring service consumption as a running operational cost, which calls for Operating expenditure (OpEx) rather than Automation of shutdown schedules or the earlier Savings Plans commitment.

Self-service provisioning addresses the separate need to create an environment without waiting for physical equipment installation because authorized teams can provision cloud resources through APIs or a console when they are needed; it is not as direct as Automation consistency for the actual requirement to repeat routine resource provisioning with fewer manual variations.

Rapid global deployment addresses the separate need to make a proven application available in a newly targeted market quickly because cloud infrastructure in multiple geographic regions reduces the need to construct local facilities; it is not as direct as Automation consistency for the actual requirement to repeat routine resource provisioning with fewer manual variations.

Elasticity addresses the separate need to increase and decrease capacity in response to changing demand because elasticity matches provisioned resources to workload demand, allowing capacity to contract after a spike; it is not as direct as Automation consistency for the actual requirement to repeat routine resource provisioning with fewer manual variations.

Operational excellence pillar is intended to improve how teams operate, monitor and continually refine workloads because operational excellence focuses on delivering business value through effective operations and continuous learning; in this design the new outcome is to use incidents and measurements to continually refine cloud operations, which calls for Frequent improvement loops rather than Operational excellence pillar or the earlier Capacity awareness.

Operations as code is intended to apply controlled repeatable procedures to infrastructure and operations because machine-readable definitions allow review, versioning and consistent execution of operations; in this design the new outcome is to use incidents and measurements to continually refine cloud operations, which calls for Frequent improvement loops rather than Operations as code or the earlier Capacity awareness.

Capacity awareness primarily supports the earlier need to understand resource demand and quotas before workload demand exceeds limits because good reliability planning accounts for capacity needs and service quotas, but the new task is to use incidents and measurements to continually refine cloud operations; that is why Frequent improvement loops is preferable here.

AWS CAF Business perspective primarily supports the earlier need to connect cloud adoption to business outcomes and investment decisions because the Business perspective aligns cloud initiatives with strategy, value and commercial priorities, but the new task is to prepare the workforce and organizational culture for cloud transformation; that is why AWS CAF People perspective is preferable here.

AWS CAF Operations perspective is intended to prepare support, monitoring and service operations for migrated workloads because the Operations perspective develops capabilities for operating workloads effectively; in this design the new outcome is to prepare the workforce and organizational culture for cloud transformation, which calls for AWS CAF People perspective rather than AWS CAF Operations perspective or the earlier AWS CAF Business perspective.

AWS CAF Governance perspective is intended to manage transformation risks, priorities and portfolio decisions because the Governance perspective addresses decision-making, program oversight and value management; in this design the new outcome is to prepare the workforce and organizational culture for cloud transformation, which calls for AWS CAF People perspective rather than AWS CAF Governance perspective or the earlier AWS CAF Business perspective.