SAP-C02 Exam Dumps - AWS Certified Solutions Architect - Professional

 This solution will provide the highest availability for the database, as the read replicas will allow the database to be available in multiple Regions, thus reducing the chances of disruption. Additionally, the promotion of the cross-Region read replica to become a standalone DB instance will ensure that the database is still available even if one of the Regions experiences disruptions.

Turning on S3 server-side encryption for the S3 bucket that the web application uses will enable encrypting the data at rest using Amazon S3 managed keys (SSE-S3)1. Creating a bucket policy that denies any unencrypted operations in the S3 bucket that the web application uses will enable enforcing encryption for all requests to the bucket2. Configuring redirection of HTTP requests to HTTPS requests in CloudFront will enable encrypting the data in transit using SSL/TLS3.

The company should attach a policy to the S3-access group to deny all S3 actions unless MFA is present. The company should request temporary credentials from AWS Security Token Service (AWS STS). The company should attach the temporary credentials in a profile that Amazon S3 will reference when the user performs actions in Amazon S3. This solution will meet the requirements because AWS STS is a service that enables you to request temporary, limited-privilege credentials for IAM users or for users that you authenticate (federated users). You can use MFA with AWS STS to provide an extra layer of security when requesting temporary credentials1. You can use the sts get-session-token AWS CLI command to request temporary credentials that include an MFA token2. You can then use these credentials with the AWS CLI to access Amazon S3 resources. To do this, you need to attach a policy to the IAM group that denies all S3 actions unless MFA is present3.You also need to create a profile in the AWS CLI configuration file that references the temporary credentials.

The other options are not correct because:

Attaching a policy to the S3 bucket to prompt the IAM user for an MFA code when the IAM user performs actions on the S3 bucket would not work because policies attached to S3 buckets cannot enforce MFA authentication. Policies attached to S3 buckets are resource-based policies that define what actions can be performed on the bucket and by whom. They do not have any logic to prompt for an MFA code or verify it.

Updating the trust policy for the S3-access group to require principals to use MFA when principals assume the group would not work because trust policies are used for roles, not groups. Trust policies are policies that define which principals can assume a role. They do not apply to groups, which are collections of IAM users that share permissions.

Creating an Amazon Route 53 Resolver DNS Firewall domain list that contains the allowed domains and configuring a DNS Firewall rule group with rules to allow or block requests based on the domain list would not help with enforcing MFA authentication for Amazon S3 actions. Amazon Route 53 Resolver DNS Firewall is a feature that enables you to filter and regulate outbound DNS traffic for your VPC. You can create reusable collections of filtering rules in DNS Firewall rule groups and associate them with your VPCs. You can specify lists of domain names to allow or block, and you can customize the responses for the DNS queries that you block. This feature is useful for controlling access to sites and blocking DNS-level threats, but not for requiring MFA authentication.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/create-asg-instance-type-requirements.html#use-attribute-based-instance-type-selection-prerequisites

(https://aws.amazon.com/compute-optimizer/pricing/,https://aws.amazon.com/systems-manager/pricing/).

https://aws.amazon.com/compute-optimizer/

Amazon SageMaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models quickly and easily1. Users can export their existing TensorFlow models and store the model artifacts in Amazon S3, a highly scalable and durable object storage service2. Users can then deploy the model to Amazon SageMaker and create an endpoint that can be invoked by the web application to provide recommendations3. This way, the solution can leverage the AI/ML capabilities of Amazon SageMaker without having to rewrite the personalization model.

AWS Elastic Beanstalk is a service that allows users to deploy and manage web applications without worrying about the infrastructure that runs those applications. Users can host their Java application in AWS Elastic Beanstalk and configure it to communicate with the Amazon SageMaker endpoint. This way, the solution can reduce the operational overhead of managing servers, load balancers, scaling, and application health monitoring.

AWS Database Migration Service (AWS DMS) is a service that helps users migrate databases to AWS quickly and securely. Users can use AWS DMS to migrate their SQL Server database to Amazon RDS for SQL Server, a fully managed relational database service that offers high availability, scalability, security, and compatibility. This way, the solution canreduce the operational overhead of managing database servers, backups, patches, and upgrades.

Option A is incorrect because using AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS is not cost-effective or scalable. AWS SMS is a service that helps users migrate on-premises workloads to AWS. However, for this use case, migrating the physical application server and the web application VMs to AWS will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option C is incorrect because using AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups is not cost-effective or scalable. AWS Application Migration Service is a service that helps users migrate applications from on-premises or other clouds to AWS without making any changes to their applications. However, for this use case, migrating the personalization model and VMs to EC2 instances will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option D is incorrect because containerizing the personalization model and the Java application and using Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy them to Amazon EKS is not necessary or cost-effective. Amazon EKS is a service that allows users to run Kubernetes on AWS without needing to install, operate, and maintain their own Kubernetes control plane or nodes. However, for this use case, containerizing and deploying the personalization model and the Java application will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk. Moreover, using S3 Glacier Deep Archive as a storage class for images will incur a high retrieval fee and latency for accessing them.

It can prevent the issue from happening again by monitoring the file system with the FreeStorageCapacity metric in Amazon CloudWatch and using Amazon EventBridge to invoke an AWS Lambda function to increase the capacity as required. This ensures that the file system always has enough free space to store user profiles and avoids reaching maximum capacity.

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/stacksets-orgs-manage-auto-deployment.html