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Difficulty: Very hardCost-Effective Network Routing and Data Transfer Optimization

A financial services company is optimizing its network architecture in the us-east-1 Region to reduce mounting AWS data transfer and routing costs. The company's main application VPC (VPC-A) contains Amazon EC2 instances in private subnets that perform the following operations:

* Retrieve 120 TB of archive data monthly from an Amazon S3 bucket located in the us-east-1 Region.
* Query configuration metadata from an Amazon DynamoDB table in the us-east-1 Region.
* Replicate 50 TB of database logs monthly to another VPC (VPC-B) in the us-east-1 Region. This traffic is currently routed through an AWS Transit Gateway.
* Fetch software updates from the internet (approximately 20 GB monthly) via a NAT Gateway.

Which combination of architectural modifications will achieve the maximum cost reduction? (Select TWO.)

  1. Create Gateway VPC Endpoints for both Amazon S3 and Amazon DynamoDB in the main application VPC, and configure the private subnet route tables to direct traffic to these endpoints.Answer
  2. Establish a VPC Peering connection between the two VPCs, and update the subnet route tables to route the database replication traffic through the peering connection instead of the Transit Gateway.Answer
  3. C
    Deploy Interface VPC Endpoints for Amazon S3 and Amazon DynamoDB in the main application VPC, and configure DNS resolution to route traffic through these endpoints.
  4. D
    Configure an AWS Site-to-Site VPN connection over the internet between the two VPCs, and update the route tables to route the database replication traffic through the VPN tunnels.
  5. E
    Replace the NAT Gateway in the main application VPC with a NAT Instance deployed on a single small EC2 instance, and route all S3 and DynamoDB traffic through it.

Answer

To minimize data transfer costs, the company should create Gateway VPC Endpoints in the main application VPC for Amazon S3 and DynamoDB, and establish a VPC Peering connection between the two VPCs to replace the Transit Gateway routing path.
The combination of creating Gateway VPC Endpoints for S3 and DynamoDB and establishing a VPC Peering connection between the two VPCs provides the most cost-effective architecture. Gateway VPC Endpoints do not charge any hourly or data processing fees, completely eliminating the NAT Gateway fees for the 120 TB of S3 traffic and DynamoDB queries. VPC Peering does not charge hourly or data processing fees for inter-VPC traffic within the same region, eliminating the $0.02 per GB processing fee charged by AWS Transit Gateway.

Step-by-Step Solution

1
Analyze S3 and DynamoDB data transfer requirements.
Identified 120 TB of S3 retrieval and frequent DynamoDB queries originating from private subnets in us-east-1.
Large-scale data traffic to S3 and DynamoDB within the same region can be optimized using Gateway VPC Endpoints, which are free and bypass NAT Gateways.
2
Evaluate inter-VPC replication traffic cost.
Identified 50 TB of inter-VPC traffic routed via Transit Gateway costing 0.02/GBindataprocessingfees(0.02/GB in data processing fees ( 1,000/month).
Since both VPCs are in the same region, replacing Transit Gateway with VPC Peering removes the processing fee while maintaining private connectivity.
3
Assess internet traffic requirements.
Software updates require 20 GB of public internet access monthly.
This low-volume public traffic should remain on the NAT Gateway as it is cost-effective at low volumes and requires external internet routing.
4
Compare alternative endpoint and routing choices.
Interface VPC Endpoints and NAT Instances would still charge for data processing or compute, whereas Gateway VPC Endpoints and VPC Peering offer the lowest cost routing paths.
Eliminating hourly and data processing fees for high-volume internal AWS traffic yields the maximum cost reduction.

Key Concept

Optimizing network routing paths and AWS endpoints to avoid data processing charges for internal and regional transfers.
Estimated Time:3m 0s
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Topics

Cost-Effective Network Routing and Data Transfer Optimization