It differs from performance efficiency, which emphasizes appropriate resource use; that caveat is associated more closely with Reliability pillar for the need to help a workload recover from failures and continue functioning correctly than with Security pillar for the requirement to protect information, systems and assets using risk-aware controls.

The best-performing option is not automatically the most sustainable or cheapest; that caveat is associated more closely with Performance efficiency pillar for the need to select and adapt resources to meet performance needs efficiently than with Security pillar for the requirement to protect information, systems and assets using risk-aware controls.

It is not limited to choosing the least expensive instance; that caveat is associated more closely with Operational excellence pillar for the need to improve how teams operate, monitor and continually refine workloads than with Security pillar for the requirement to protect information, systems and assets using risk-aware controls.

Scalability is intended to support larger workloads by adding capacity as requirements grow because scalability is the ability of a system to handle growth by increasing resources or distributing work; in this design the new outcome is to make a proven application available in a newly targeted market quickly, which calls for Rapid global deployment rather than Scalability or the earlier High availability.

Elasticity is intended 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; in this design the new outcome is to make a proven application available in a newly targeted market quickly, which calls for Rapid global deployment rather than Elasticity or the earlier High availability.

High availability primarily supports the earlier need to keep an application accessible despite the failure of an individual component because high availability relies on redundancy and failure isolation so a single failure need not interrupt service, but the new task is to make a proven application available in a newly targeted market quickly; that is why Rapid global deployment is preferable here.

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 consider competing architectural priorities instead of optimizing one metric blindly; understand resource demand and quotas before workload demand exceeds limits.

Multi-AZ redundancy addresses a different objective, to avoid tying service availability to one Availability Zone, because deploying suitable redundant resources in multiple AZs isolates many facility-level failures; the checklist instead requires the distinct outcomes to consider competing architectural priorities instead of optimizing one metric blindly; understand resource demand and quotas before workload demand exceeds limits.

Vertical scaling addresses a different objective, to increase the capacity of an existing compute resource, because vertical scaling changes the size or power of one instance or server; the checklist instead requires the distinct outcomes to consider competing architectural priorities instead of optimizing one metric blindly; understand resource demand and quotas before workload demand exceeds limits.

That description belongs to Avoided data-center costs because migrating suitable workloads can shift responsibility for physical facility operation to AWS; it is used to reduce expenses for facility space, power, cooling and physical maintenance, not to explain how Cost visibility provides the function to understand resource expenses before changing deployment decisions.

That description belongs to Capital expenditure (CapEx) because capEx involves purchasing owned assets such as servers and data-center equipment before the benefits are consumed; it is used to recognize upfront infrastructure purchase as a fixed investment, not to explain how Cost visibility provides the function to understand resource expenses before changing deployment decisions.

That description belongs to Demand forecasting because forecasts help teams plan spend and procurement based on usage trends; it is used to estimate future usage to avoid unplanned commitment or capacity decisions, not to explain how Cost visibility provides the function to understand resource expenses before changing deployment decisions.

AWS CAF Security perspective is intended to embed protection, risk practices and security capabilities into migration because the Security perspective helps establish security governance and resilient protective controls; 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 AWS CAF Security perspective or the earlier AWS Application Migration Service.

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 Application Migration Service.

AWS Application Migration Service primarily supports the earlier need to replicate source servers for a largely lift-and-shift migration because application Migration Service supports server migration with continuous replication and automated conversion to run on AWS, 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.

AWS Cloud Adoption Framework (AWS CAF) primarily supports the earlier need to structure the organizational changes needed for cloud adoption because AWS CAF organizes transformation capabilities into Business, People, Governance, Platform, Security and Operations perspectives, 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 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 Cloud Adoption Framework (AWS CAF).

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 Cloud Adoption Framework (AWS CAF).

Elasticity is intended 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; in this design the new outcome is to support larger workloads by adding capacity as requirements grow, which calls for Scalability rather than Elasticity or the earlier Reduced undifferentiated heavy lifting.

Reduced undifferentiated heavy lifting primarily supports the earlier need to spend less staff effort maintaining generic infrastructure components because managed AWS services take over selected operational tasks such as platform maintenance, but the new task is to support larger workloads by adding capacity as requirements grow; that is why Scalability is preferable here.

Elastic test environments is intended to scale test capacity up for an evaluation then stop consuming it because temporary cloud test resources can be provisioned and removed with demand; in this design the new outcome is to support larger workloads by adding capacity as requirements grow, which calls for Scalability rather than Elastic test environments or the earlier Reduced undifferentiated heavy lifting.

Multi-AZ redundancy addresses a different objective, to avoid tying service availability to one Availability Zone, because deploying suitable redundant resources in multiple AZs isolates many facility-level failures; the checklist instead requires the distinct outcomes to allow application components to fail or scale with less direct dependency on each other; consider competing architectural priorities instead of optimizing one metric blindly.

Operations as code addresses a different objective, to apply controlled repeatable procedures to infrastructure and operations, because machine-readable definitions allow review, versioning and consistent execution of operations; the checklist instead requires the distinct outcomes to allow application components to fail or scale with less direct dependency on each other; consider competing architectural priorities instead of optimizing one metric blindly.

Right-sized architecture addresses a different objective, to match provisioned resources to measured workload requirements, because right sizing avoids both excess capacity and insufficient capacity for expected service levels; the checklist instead requires the distinct outcomes to allow application components to fail or scale with less direct dependency on each other; consider competing architectural priorities instead of optimizing one metric blindly.

Cost visibility addresses the separate 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; it is not as direct as Spot capacity economics for the actual requirement to save on flexible interruptible EC2 computing.

Operating expenditure (OpEx) addresses the separate need to treat recurring service consumption as a running operational cost because opEx reflects spending as services and computing resources are consumed; it is not as direct as Spot capacity economics for the actual requirement to save on flexible interruptible EC2 computing.

Return on investment (ROI) addresses the separate need to evaluate expected financial benefit relative to an investment because rOI compares gains with associated investment costs for a business decision; it is not as direct as Spot capacity economics for the actual requirement to save on flexible interruptible EC2 computing.

Automation of shutdown schedules addresses the separate need to avoid running nonproduction compute unnecessarily because scheduled automation can stop or remove eligible idle resources when they are not required; it is not as direct as Spot capacity economics for the actual requirement to save on flexible interruptible EC2 computing.

Right-sized architecture addresses the separate need to match provisioned resources to measured workload requirements because right sizing avoids both excess capacity and insufficient capacity for expected service levels; it is not as direct as Design for failure for the actual requirement to plan for individual components or zones becoming unavailable.

Security pillar addresses the separate need to protect information, systems and assets using risk-aware controls because the security pillar addresses identities, detection, infrastructure and data protection, and incident response; it is not as direct as Design for failure for the actual requirement to plan for individual components or zones becoming unavailable.

Horizontal scaling addresses the separate need to spread work across additional independent resources because horizontal scaling increases the number of workers or instances serving a workload; it is not as direct as Design for failure for the actual requirement to plan for individual components or zones becoming unavailable.

Spot capacity economics primarily supports the earlier need to save on flexible interruptible EC2 computing because Spot Instances exploit spare EC2 capacity at discounted rates with interruption risk, but the new task is to obtain eligible compute discounts in exchange for a spending commitment; that is why Savings Plans commitment is preferable here.

Resource tagging for accountability is intended to attribute cloud spending to teams and projects because consistent resource tags support reporting and ownership when activated for cost allocation; in this design the new outcome is to obtain eligible compute discounts in exchange for a spending commitment, which calls for Savings Plans commitment rather than Resource tagging for accountability or the earlier Spot capacity economics.

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 obtain eligible compute discounts in exchange for a spending commitment, which calls for Savings Plans commitment rather than Automation of shutdown schedules or the earlier Spot capacity economics.