Amazon SAP-C02 Practice Test - Questions Answers, Page 45

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.Reference:What is a Gateway Load Balancer?,Gateway load balancer - Azure Load Balancer,Introducing Azure Gateway Load Balancer: Deploy and scale network ...

The best solution is to create an RSA key pair that is dedicated to the data-handling microservice and upload the public key to the CloudFront distribution. Then, create a field-level encryption profile and a configuration, and add the configuration to the CloudFront cache behavior. This solution will ensure that the sensitive data is encrypted at the edge locations of CloudFront, close to the end users, and remains encrypted throughout the application stack. Only the data-handling microservice, which has access to the private key of the RSA key pair, can decrypt the data. This solution does not require any additional resources or code changes, and leverages the built-in feature of CloudFront field-level encryption. For more information about CloudFront field-level encryption, seeUsing field-level encryption to help protect sensitive data.

The best solution is to upload files from the mobile software directly to Amazon S3, use S3 event notifications to create a message in an Amazon Simple Queue Service (Amazon SQS) standard queue, and invoke an AWS Lambda function to perform image processing when a message is available in the queue. This solution will ensure that image processing can scale to handle the load, as Amazon S3 can store any amount of data and handle concurrent uploads, Amazon SQS can buffer the messages and deliver them reliably, and AWS Lambda can run code without provisioning or managing servers and scale automatically based on the demand. This solution will also notify the user when processing is complete by sending a push notification to the mobile app using Amazon Simple Notification Service (Amazon SNS), which is a web service that enables applications to send and receive notifications from the cloud. This solution is more cost-effective than using Amazon MQ, which is a managed message broker service for Apache ActiveMQ that requires a dedicated broker instance, or S3 Batch Operations, which is a feature that allows users to perform bulk actions on S3 objects, such as copying or tagging, but does not support custom code execution. This solution is also more suitable than using Amazon Simple Email Service (Amazon SES), which is a web service that enables applications to send and receive email messages, but does not support push notifications for mobile devices.Reference:Amazon S3 Documentation,Amazon SQS Documentation,AWS Lambda Documentation,Amazon SNS Documentation

Deploy DataSync Agent:

Install the AWS DataSync agent as a VM in your Hyper-V environment. This agent facilitates the data transfer between your on-premises storage and AWS.

Configure Source and Destination:

Set up the source location to point to your on-premises NFS server where the image batches are stored.

Configure the destination location to be an Amazon S3 bucket with the Glacier Deep Archive storage class. This storage class is cost-effective for long-term storage with retrieval times of up to 12 hours.

Create DataSync Tasks:

Create and configure DataSync tasks to manage the data transfer. Schedule these tasks to run during non-business hours to minimize bandwidth usage during peak times. The tasks will handle the copying of data batches from the NFS server to the S3 bucket.

Set Bandwidth Limits:

In the DataSync configuration, set bandwidth limits to control the amount of data being transferred at any given time. This ensures that your network's performance is not adversely affected during business hours.

Delete On-Premises Data:

After successfully copying the data to S3 Glacier Deep Archive, configure the DataSync task to delete the data from your on-premises NFS server. This helps manage storage capacity on-premises and ensures data is securely archived on AWS.

This approach leverages AWS DataSync for efficient, secure, and automated data transfer, and S3 Glacier Deep Archive for cost-effective long-term storage.

Reference

AWS DataSync Overview41.

AWS Storage Blog on DataSync Migration40.

Amazon S3 Transfer Acceleration Documentation42.

Enable DynamoDB Auto Scaling:

Navigate to the DynamoDB console and select the table experiencing high demand.

Go to the 'Capacity' tab and enable auto scaling for both read and write capacity units. Auto scaling adjusts the provisioned throughput capacity automatically in response to actual traffic patterns, ensuring the table can handle the increased load.

Configure Auto Scaling Policies:

Set the minimum and maximum capacity units to define the range within which auto scaling can adjust the provisioned throughput.

Specify target utilization percentages for read and write operations, typically around 70%, to maintain a balance between performance and cost.

Monitor and Adjust:

Use Amazon CloudWatch to monitor the auto scaling activity and ensure it is effectively handling the increased demand.

Adjust the auto scaling settings if necessary to better match the traffic patterns and application requirements.

By enabling DynamoDB auto scaling, you ensure that the database can handle the fluctuating traffic volumes without manual intervention, improving response times and reducing errors.

Reference

AWS Compute Blog on Using API Gateway as a Proxy for DynamoDB60.

AWS Database Blog on DynamoDB Accelerator (DAX)59.

AWS Application Migration Service:

AWS Application Migration Service (MGN) facilitates the migration of virtual machines (VMs) to AWS without installing additional software on the VMs. This agentless service helps in seamlessly migrating workloads to AWS.

AWS Migration Hub Strategy Recommendations:

AWS Migration Hub Strategy Recommendations offer insights and guidance for planning and implementing migration strategies. It helps in generating a Total Cost of Ownership (TCO) report by automatically importing discovery data from the VMs.

Generating the TCO Report:

The combined use of AWS Application Migration Service and Migration Hub Strategy Recommendations enables the automatic import of discovery data and the generation of an accurate TCO report, ensuring a smooth and cost-effective migration process.

Reference

AWS Migration Hub Strategy Recommendations (AWS Documentation).

GitHub Webhooks to Trigger CodePipeline:

Configure GitHub webhooks to trigger the AWS CodePipeline pipeline. This ensures that every code push to the repository automatically triggers the pipeline, initiating the build and deployment process.

Unit Testing with Jenkins and AWS CodeBuild:

Use Jenkins integrated with the AWS CodeBuild plugin to perform unit testing. Jenkins will manage the build process, and the results will be handled by CodeBuild.

Notifications for Bad Builds:

Configure Amazon SNS (Simple Notification Service) to send alerts for any failed builds. This keeps the engineering team informed of build issues immediately, allowing for quick resolutions.

Blue/Green Deployment with AWS CodeDeploy:

Utilize AWS CodeDeploy with a blue/green deployment strategy. This method reduces downtime and risk by running two identical production environments (blue and green). CodeDeploy shifts traffic between these environments, allowing you to test in the new environment (green) while the old environment (blue) remains live. If issues arise, you can quickly roll back to the previous environment.

This solution provides seamless, zero-downtime deployments, and the ability to quickly roll back changes if necessary, fulfilling the requirements of the startup company.

Reference

AWS DevOps Blog on Integrating Jenkins with AWS CodeBuild and CodeDeploy32.

Plain English Guide to AWS CodePipeline with GitHub33.

Jenkins Plugin for AWS CodePipeline34.

Store Secret in AWS Secrets Manager:

Create a random string in AWS Secrets Manager to be used as a custom HTTP header value.

Set Up Automatic Rotation:

Implement a Lambda function to handle automatic rotation of the secret in AWS Secrets Manager, ensuring the header value remains secure.

Configure CloudFront Custom Header:

In the CloudFront distribution settings, configure an origin custom header with the name and value from AWS Secrets Manager. This header will be included in requests forwarded to the ALB.

Create AWS WAF Web ACL:

Create a Web ACL in AWS WAF with a string match rule to allow requests that include the custom header with the correct value.

Associate the Web ACL with the ALB to filter incoming traffic based on the custom header.

By using this method, you can ensure that only requests coming through CloudFront (which injects the custom header) can reach the ALB, enhancing the origin security

Create AWS Organization:

In the AWS Management Console, navigate to AWS Organizations and create a new organization in the parent account.

Invite LOB Accounts:

Invite each Line of Business (LOB) account to join the organization. This allows centralized management and governance of all accounts.

Enable Consolidated Billing:

Enable consolidated billing in the billing console of the parent account. Link all LOB accounts to ensure a single consolidated invoice that breaks down costs per account.

Apply Service Control Policies (SCPs):

Implement Service Control Policies (SCPs) to define the services and features permitted for each LOB account as per the governance policy, while still delegating full administrative permissions to the LOB accounts.

By consolidating billing and using AWS Organizations, the company can achieve centralized billing and governance while maintaining independent administrative control for each LOB account