NoSQL access-pattern design primarily supports the earlier need to design a database around known key lookups and throughput patterns because DynamoDB data modeling focuses on partition keys indexes and predictable access paths, but the new task is to restore supported table data to a recent earlier point; that is why DynamoDB point-in-time recovery is preferable here.

Amazon Redshift is intended to analyze large structured data sets in a columnar warehouse because redshift provides a cloud data warehouse optimized for analytical SQL workloads; in this design the new outcome is to restore supported table data to a recent earlier point, which calls for DynamoDB point-in-time recovery rather than Amazon Redshift or the earlier NoSQL access-pattern design.

Amazon ElastiCache is intended to cache frequently accessed application data in memory because elastiCache offers managed in-memory caching engines to reduce repetitive backend reads; in this design the new outcome is to restore supported table data to a recent earlier point, which calls for DynamoDB point-in-time recovery rather than Amazon ElastiCache or the earlier NoSQL access-pattern design.

Amazon EKS addresses the separate need to run Kubernetes-based container workloads using an AWS-managed control plane because eKS integrates managed Kubernetes cluster control plane services with AWS infrastructure; it is not as direct as EC2 placement groups for the actual requirement to influence how supported instances are distributed across underlying hardware.

AWS Elastic Beanstalk addresses the separate need to deploy supported applications while the platform provisions common resources because elastic Beanstalk simplifies web-app deployment and orchestrates the supporting AWS components; it is not as direct as EC2 placement groups for the actual requirement to influence how supported instances are distributed across underlying hardware.

AWS Fargate addresses the separate need to run container tasks without managing EC2 container hosts because fargate supplies serverless compute for supported ECS and EKS workloads; it is not as direct as EC2 placement groups for the actual requirement to influence how supported instances are distributed across underlying hardware.

That description belongs to S3 Glacier Deep Archive because deep Archive is designed for extremely infrequent access and long retention; it is used to retain long-lived archives at very low storage cost when restoration can be slow, not to explain how S3 Glacier Instant Retrieval provides the function to keep rarely accessed archives with millisecond access when requested.

That description belongs to S3 bucket policy because bucket policies are resource-based JSON permissions for supported S3 actions; it is used to control allowed and denied access to an S3 bucket and its objects, not to explain how S3 Glacier Instant Retrieval provides the function to keep rarely accessed archives with millisecond access when requested.

That description belongs to Amazon S3 Intelligent-Tiering because intelligent-Tiering can move eligible objects between access tiers based on use; it is used to optimize storage charges when object access patterns are unpredictable, not to explain how S3 Glacier Instant Retrieval provides the function to keep rarely accessed archives with millisecond access when requested.

Data catalog metadata is intended to discover schemas and locations of datasets for analytics engines because a data catalog records metadata used by compatible query and ETL tools; in this design the new outcome is to collect diverse raw and processed datasets in object storage, which calls for Data lake on Amazon S3 rather than Data catalog metadata or the earlier AWS Lake Formation.

AWS Lake Formation primarily supports the earlier need to govern data lakes with access and catalog integration because lake Formation helps establish and secure data-lake environments using AWS data services, but the new task is to collect diverse raw and processed datasets in object storage; that is why Data lake on Amazon S3 is preferable here.

ETL data preparation is intended to clean transform and load datasets before reporting because eTL services such as AWS Glue transform records into analysis-ready formats; in this design the new outcome is to collect diverse raw and processed datasets in object storage, which calls for Data lake on Amazon S3 rather than ETL data preparation or the earlier AWS Lake Formation.

Amazon Route 53 is intended to resolve application domain names and apply supported DNS routing policies because Route 53 is a managed DNS and domain service with traffic-routing and health-check features; in this design the new outcome is to select DNS answers based on the geographic origin of DNS queries, which calls for Route 53 geolocation routing rather than Amazon Route 53 or the earlier Route 53 latency routing.

Route 53 weighted routing is intended to distribute DNS responses among configured endpoints according to weights because weighted routing directs shares of DNS answers toward configured records; in this design the new outcome is to select DNS answers based on the geographic origin of DNS queries, which calls for Route 53 geolocation routing rather than Route 53 weighted routing or the earlier Route 53 latency routing.

Route 53 latency routing primarily supports the earlier need to answer DNS requests with an endpoint selected for lower network latency because latency-based policies choose among configured Region records using AWS latency observations, but the new task is to select DNS answers based on the geographic origin of DNS queries; that is why Route 53 geolocation routing is preferable here.

That description belongs to Amazon EC2 because EC2 provides configurable compute instances where customers manage guest OS and application software; it is used to operate virtual servers with explicit OS and instance management, not to explain how Horizontal EC2 scaling provides the function to add parallel instance capacity for a distributed application.

That description belongs to Elasticity through scaling policies because metric-driven policies can change running capacity in response to utilization or demand; it is used to match compute capacity to traffic fluctuations automatically, not to explain how Horizontal EC2 scaling provides the function to add parallel instance capacity for a distributed application.

That description belongs to Vertical EC2 scaling because vertical scaling provides more CPU or RAM within a single virtual machine; it is used to increase resource capacity by changing the instance type, not to explain how Horizontal EC2 scaling provides the function to add parallel instance capacity for a distributed application.

Amazon Athena is intended to query supported data in S3 using serverless SQL because athena runs interactive SQL queries without provisioning a dedicated database server; in this design the new outcome is to run analytical workloads in a purpose-built data warehouse, which calls for Amazon Redshift rather than Amazon Athena or the earlier ETL data preparation.

ETL data preparation primarily supports the earlier need to clean transform and load datasets before reporting because eTL services such as AWS Glue transform records into analysis-ready formats, but the new task is to run analytical workloads in a purpose-built data warehouse; that is why Amazon Redshift is preferable here.

AWS Lake Formation is intended to govern data lakes with access and catalog integration because lake Formation helps establish and secure data-lake environments using AWS data services; in this design the new outcome is to run analytical workloads in a purpose-built data warehouse, which calls for Amazon Redshift rather than AWS Lake Formation or the earlier ETL data preparation.

EBS volume encryption addresses a different objective, to protect block storage with supported at-rest encryption, because encrypted EBS volumes use integrated key management for data at rest; the checklist instead requires the distinct outcomes to mount elastic shared file storage from multiple Linux workloads; keep rarely accessed archives with millisecond access when requested.

Amazon EBS addresses a different objective, to attach persistent block-level storage to supported EC2 instances, because EBS volumes provide durable block storage within an Availability Zone with supported snapshot options; the checklist instead requires the distinct outcomes to mount elastic shared file storage from multiple Linux workloads; keep rarely accessed archives with millisecond access when requested.

Amazon FSx for Windows File Server addresses a different objective, to provide managed SMB file shares for Windows workloads, because fSx for Windows File Server supports Windows file protocols and Active Directory integration; the checklist instead requires the distinct outcomes to mount elastic shared file storage from multiple Linux workloads; keep rarely accessed archives with millisecond access when requested.

Edge location is intended to deliver cached content closer to geographically dispersed end users because edge sites support services such as CloudFront close to viewers to reduce delivery latency; in this design the new outcome is to provide the source from which edge caches fetch content on a miss, which calls for CloudFront origin rather than Edge location or the earlier Cross-Region replication.

AWS Region is intended to select a geographic area in which to place a workload because an AWS Region is a distinct geographic location containing multiple Availability Zones; in this design the new outcome is to provide the source from which edge caches fetch content on a miss, which calls for CloudFront origin rather than AWS Region or the earlier Cross-Region replication.

Cross-Region replication primarily supports the earlier need to maintain copies of selected data outside its source Region because cross-Region replication moves eligible objects or database data into a different geography when configured, but the new task is to provide the source from which edge caches fetch content on a miss; that is why CloudFront origin is preferable here.

Database on Amazon EC2 is intended to retain direct control of database host OS and engine installation because self-managing a database on EC2 leaves patching, backups and high availability largely to the customer; in this design the new outcome is to use a cloud-optimized relational engine compatible with MySQL or PostgreSQL, which calls for Amazon Aurora rather than Database on Amazon EC2 or the earlier Amazon RDS.

Amazon DocumentDB is intended to run JSON document workloads using MongoDB-compatible APIs because documentDB offers managed document database capabilities for suitable MongoDB workloads; in this design the new outcome is to use a cloud-optimized relational engine compatible with MySQL or PostgreSQL, which calls for Amazon Aurora rather than Amazon DocumentDB or the earlier Amazon RDS.

Amazon RDS primarily supports the earlier need to operate a managed relational database engine because RDS automates selected relational database operations including backup and patching, but the new task is to use a cloud-optimized relational engine compatible with MySQL or PostgreSQL; that is why Amazon Aurora is preferable here.

That description belongs to AWS Lambda because lambda runs functions in managed execution environments with service-defined execution constraints; it is used to execute event-triggered code without managing virtual servers, not to explain how Application Load Balancer provides the function to route HTTP and HTTPS requests using application-layer rules.

That description belongs to Amazon EC2 because EC2 provides configurable compute instances where customers manage guest OS and application software; it is used to operate virtual servers with explicit OS and instance management, not to explain how Application Load Balancer provides the function to route HTTP and HTTPS requests using application-layer rules.

That description belongs to Horizontal EC2 scaling because horizontal scaling adds multiple instances that can share workload when architecture supports it; it is used to add parallel instance capacity for a distributed application, not to explain how Application Load Balancer provides the function to route HTTP and HTTPS requests using application-layer rules.