Amazon Web Services SAP-C02 - AWS Certified Solutions Architect - Professional

The best solution is to deploy two firewall appliances into the shared services VPC, each in a separate Availability Zone, and create a new Gateway Load Balancer to distribute traffic to them. A Gateway Load Balancer is designed for high performance and high availability scenarios with third-party network virtual appliances, such as firewalls. It operates at the network layer and maintains flow stickiness and symmetry to a specific appliance instance. It also uses the GENEVE protocol to encapsulate traffic between the load balancer and the appliances. To route traffic from other VPCs to the Gateway Load Balancer, a VPC Gateway Load Balancer endpoint is needed. This is a VPC endpoint that provides private connectivity between the appliances in the shared services VPC and the application servers in other VPCs. The endpoint must be added as the next hop in the route table for the shared services VPC. This solution ensures reliability and minimizes failover time between firewall appliances within a single AWS Region. References: What is a Gateway Load Balancer?, Gateway load balancer - Azure Load Balancer, Introducing Azure Gateway Load Balancer: Deploy and scale network …

Using Tag Editor to remediate untagged resources is a Best Practice (Page 14 or AWS Tagging Best Practices WhitePaper). However, that is were answer A stops. It doesn ' t address the requirement of " Management requires cost center numbers and project ID number for all existing and future DynamoDB tables and RDS instances " . That is where Answer C comes in and addresses that requirement with SCPs in the company ' s AWS Organization. AWS Tagging Best Practices -https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf

The correct answer is B.

B. This solution meets the requirements because it uses AWS Cost Anomaly Detection, which is a feature of AWS Cost Management that uses machine learning to identify and alert on anomalous spend and usage patterns. By configuring a monitor type of AWS Service and applying a filter of Amazon EC2, the solution can track the EC2 usage as a metric across the organization’s accounts.By configuring an alert subscription with a threshold of 10%, the solution can notify the architecture team via email or AmazonSNS if the EC2 usage is more than 10% higher than the average usage for the last 30 days12

A. This solution is incorrect because it uses AWS Budgets, which is a feature of AWS Cost Management that helps to plan and track costs and usage. However, AWS Budgets does not support usage type of EC2 running hours as a budget type. The only supported usage types are Amazon S3 storage, Amazon EC2 RI utilization, and Amazon EC2 RI coverage. Moreover, AWS Budgets does not support setting the budget amount based on the reported average usage from AWS Cost Explorer.The budget amount has to be a fixed or variable value34

C. This solution is incorrect because it uses AWS Trusted Advisor, which is a feature of AWS Premium Support that provides recommendations to follow best practices for cost optimization, security, performance, and fault tolerance. However, AWS Trusted Advisor does not support configuring custom alerts based on EC2 usage or average usage for the last 30 days.The only supported alerts are based on predefined checks and thresholds that are applied to all services and resources in the account56

D. This solution is incorrect because it uses Amazon Detective, which is a service that helps to analyze and visualize security data to investigate potential security issues. However, Amazon Detective does not support configuring EC2 usage anomaly alerts basedon average usage for the last 30 days.The only supported alerts are based on GuardDuty findings and other security-related events that are detected by machine learning models78

Aurora Auto Scaling enables your Aurora DB cluster to handle sudden increases in connectivity or workload. When the connectivity or workload decreases, Aurora AutoScaling removes unnecessary Aurora Replicas so that you don ' t pay for unused provisioned DB instances

Using AWS Transfer Family with Amazon S3 storage eliminates the need to manage file servers and offers a scalable and durable file storage solution.

S3 event notifications automatically trigger the AWS Glue transformation jobs upon file arrival, eliminating manual job scheduling.

AWS Glue jobs can be configured to send a notification to an Amazon SQS queue after completion, maintaining the messaging workflow with minimal operational overhead.

This fully managed approach simplifies the architecture and leverages AWS-native automation.

The AWS Application Discovery Service can help plan migration projects by collecting data about on-premises servers, such as configuration, performance, and network connections. The data collection agent is a lightweight software that can be installed on each server to gather this information. This option is more cost-effective than agentless discovery, which requires deploying a virtual appliance in the VMware environment, or using CloudWatch agent, which incurs additional charges for CloudWatch Logs. Scanning the servers over a VPN is not a valid option for AWS Application Discovery Service. References: What is AWS Application Discovery Service?, Data collection methods

Answer B (AWS Global Accelerator) best meets both requirements: fastest failover from application failures and the lowest response time for global users. Global Accelerator is designed for global, latency-sensitive, highly available applications. It uses static Anycast IP addresses so users enter the AWS network at the nearest edge location, then traffic is carried over the AWS global backbone to the closest healthy regional endpoint (in this case, each Region’s ALB). It continuously performs health checks and can rapidly shift traffic away from unhealthy endpoints, which helps “handle application failures quickly” without being constrained by DNS caching behavior.

Why the other options are less suitable:

C (Route 53 latency-based routing + health checks) can improve routing and failover, but it is still DNS-based. DNS-based failover can be delayed by DNS resolver caching and TTL propagation, meaning failover may not be as fast or as consistent as Global Accelerator for rapid recovery.

A (CloudFront with origin group) can provide origin failover and performance benefits mainly for cacheable or edge-optimizable content. For a payment processing API (typically dynamic and not cache-friendly), CloudFront is not the primary service for achieving the least response time globally with fast multi-Region failover compared with Global Accelerator’s Anycast + edge routing model.

D (AWS Cloud WAN) addresses private network connectivity and centralized network policy across Regions/VPCs. It does not directly optimize public user-to-application latency or provide the same kind of edge-based, Anycast-driven rapid failover for internet-facing API endpoints.

This option uses AWS DataSync to replicate the on-premises images to the EFS file system over the Direct Connect connection. AWS DataSync is a service that automates and accelerates data transfer between on-premises storage systems and AWS storage services. It can transfer data to and from Amazon EFS, Amazon FSx for Windows File Server, and Amazon S3. To use AWS DataSync, the company needs to deploy an AWS DataSync agent to an on-premises server that has access to the NFS file system. The agent connects to the AWS DataSync service endpoint in the AWS Region where the EFS file system is located. The company can use an AWS PrivateLink interface endpoint to connect to the service endpoint securely and privately over the Direct Connect connection. The company can then create a task in AWS DataSync that specifies the source location (the NFS file system), the destination location (the EFS file system), and the options for the data transfer (such as schedule, bandwidth limit, and verification). AWS DataSync will then perform the data transfer efficiently and securely, using encryption in transit and at rest.

C: Multipart uploads can leave incomplete parts behind, which incur storage costs. Expiring them after 7 days minimizes waste and saves cost.

E: Since objects are infrequently accessed after 180 days, transitioning toS3 Standard-IAis cost-effective, especially for large files > 128 KB (your 100 MB+ files qualify).

Ais for shifting download cost, not reducing your S3 storage expenses.

Bhelps with upload speed butincreases cost.

Dis too aggressive; Glacier is not suited for access patterns within the first few days.

The data scientists’ EC2 instances in a separate account must access S3 data in a controlled way that satisfies the policy: only authorized networks may access the IoT data. “Authorized networks” in this context means traffic must originate from approved VPCs and must not traverse the public internet.

First, traffic from the EC2 instances to S3 must stay on the AWS network without using public endpoints. A gateway VPC endpoint for S3 in the data scientists’ VPC (option A) allows EC2 instances in that VPC to reach S3 over private connectivity. When using a gateway VPC endpoint, the route tables of the subnets associated with the endpoint automatically route S3 traffic through the endpoint. This ensures that access to S3 is from an authorized network (the VPC) instead of from the public internet.

Second, access must be constrained such that only calls made through the intended S3 access mechanism are allowed. The data lake bucket already has an S3 access point. S3 access points can be restricted by policy and can be referenced in bucket policies through the s3:DataAccessPointArn condition key. By using an S3 bucket policy that allows s3:GetObject only when s3:DataAccessPointArn matches the expected access point ARN (option E), the company can enforce that all valid access uses the approved access point configuration. Combined with the access point’s own network configuration and the VPC endpoint, access is limited to authorized networks.

Option B, blocking public access on the access point, is a good general security practice but does not by itself guarantee that access is restricted to authorized VPC networks. The main enforcement must be through VPC endpoints and bucket/access point policies.

Option C denies s3:GetObject when s3:DataAccessPointArn is a valid access point ARN, which is the opposite of what is required. This would prevent intended access via the access point rather than allow it.

Option D is not correct because gateway VPC endpoints for S3 are configured via endpoint associations with route tables, not by directly routing to an access point in the route table. Traffic to S3 is routed to the VPC endpoint, and the endpoint plus S3 policies determine access.

Therefore, creating a gateway VPC endpoint for S3 in the data scientists’ VPC (option A) and using a bucket policy condition on s3:DataAccessPointArn to allow access only through the authorized access point (option E) together meet the requirement of secure, network-restricted access from authorized networks.

However A ' s explanation is incorrect -https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

" SCPs are similar to AWS Identity and Access Management (IAM) permission policies and use almost the same syntax. However, an SCP never grants permissions. "

SCPs alone are not sufficient to granting permissions to the accounts in your organization. No permissions are granted by an SCP. An SCP defines a guardrail, or sets limits, on the actions that the account ' s administrator can delegate to the IAM users and roles in the affected accounts. The administrator must still attach identity-based or resource-based policies to IAM users or roles, or to the resources in your accounts to actually grant permissions. The effective permissions are the logical intersection between what is allowed by the SCP and what is allowed by the IAM and resource-based policies.

https://aws.amazon.com/blogs/aws/use-cloudformation-stacksets-to-provision-resources-across-multiple-aws-accounts-and-regions/

The best option is to use Amazon SQS and AWS Lambda to create a serverless order processing application. Amazon SQS is a fully managed message queue service that can decouple the order receiving and processing components, making the application more scalable and fault-tolerant. AWS Lambda is a serverless compute service that can automatically scale to handle the incoming messages from the SQS queue and process them according to the business logic. AWS Lambda can also integrate with Amazon DynamoDB to store the processed orders in a fast and flexible NoSQL database. This approach eliminates the need to provision, manage, or scale any servers or containers, and reduces the operational overhead and cost.

Option A is not reliable because using an EC2-hosted database to receive the orders introduces a single point of failure and a scalability bottleneck. EC2 instances also require more management and configuration than serverless services.

Option C is not reliable because using AWS Step Functions to receive the orders adds unnecessary complexity and cost to the application. AWS Step Functions is a service that coordinates multiple AWS services into a serverless workflow, but it is not designed to handle high-volume, sporadic, or unpredictable traffic patterns. AWS Step Functions also charges per state transition, which can be expensive for a large number of orders. Launching an ECS container to process each order also requires more resources and management than invoking a Lambda function.

Option D is not reliable because using Amazon Kinesis Data Streams to receive the orders is not suitable for this use case. Amazon Kinesis Data Streams is a service that enables real-time processing of streaming data at scale, but it is not meant for asynchronous message queuing. Amazon Kinesis Data Streams requires consumers to poll the data from the stream, which can introduce latency and complexity. Amazon Kinesis Data Streams also charges per shard hour, which can be expensive for a sporadic traffic pattern.

Amazon SQS

AWS Lambda

Amazon DynamoDB

AWS Step Functions

Amazon ECS