Amazon Web Services braindumps aws Certified Professional Sap-c02 Exam Dumps by Elowen q126 vce pdf

Page: 5 / 46

Exam Name:	AWS Certified Solutions Architect - Professional
Exam Code:	SAP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	625 Q&A's	Shared By:	elowen

Question 20

A company has created an OU in AWS Organizations for each of its engineering teams Each OU owns multiple AWS accounts. The organization has hundreds of AWS accounts A solutions architect must design a solution so that each OU can view a breakdown of usage costs across its AWS accounts. Which solution meets these requirements?

Options:

Create an AWS Cost and Usage Report (CUR) for each OU by using AWS Resource Access Manager Allow each team to visualize the CUR through an Amazon QuickSight dashboard.

Create an AWS Cost and Usage Report (CUR) from the AWS Organizations management account- Allow each team to visualize the CUR through an Amazon QuickSight dashboard

Create an AWS Cost and Usage Report (CUR) in each AWS Organizations member account Allow each team to visualize the CUR through an Amazon QuickSight dashboard.

Create an AWS Cost and Usage Report (CUR) by using AWS Systems Manager Allow each team to visualize the CUR through Systems Manager OpsCenter dashboards

Discussion

Answer:

Explanation:

https://docs.aws.amazon.com/cur/latest/userguide/billing-cur-limits.html

The requirement is to allow each OU (which contains multiple accounts) to view a cost breakdown across its accounts. In an AWS Organizations setup with consolidated billing, the management account is the central place where organization-level cost and usage reporting is configured and aggregated. The Cost and Usage Report is designed to produce the most detailed cost and usage dataset, and it can include charges for linked (member) accounts. A single CUR created from the management account can include all accounts in the organization, and then filtering can be applied to show cost breakdowns per OU or per team.

Once the CUR is delivered to Amazon S3, analytics tools such as Amazon Athena and Amazon QuickSight can be used to create dashboards. Teams can be provided access to the dataset or curated views and can filter by linked account, tags, and other dimensions. With appropriate cost allocation tags and organizational mapping (for example, account-to-OU mapping maintained in reporting logic), each OU can see its consolidated view.

Option B meets the requirements because it creates one CUR centrally from the management account and uses QuickSight for visualization. This approach is scalable for hundreds of accounts and avoids duplicating CUR configuration in each member account.

Option A is incorrect because AWS Resource Access Manager is not used to create CURs per OU. CUR is not an OU-scoped resource created via RAM.

Option C is not operationally efficient because it requires creating and managing CURs in each member account, producing fragmented datasets and significantly increasing overhead at hundreds of accounts. It also makes it harder to generate a single view per OU across accounts.

Option D is incorrect because Systems Manager does not create CURs, and OpsCenter dashboards are not the standard tool for cost and usage reporting.

Therefore, a centrally created CUR in the management account with QuickSight visualization is the correct solution.

[References:AWS documentation on AWS Cost and Usage Report creation from the AWS Organizations management account and inclusion of linked accounts.AWS documentation on using Amazon QuickSight (often with CUR data stored in S3 and queried via Athena) to build cost dashboards and filter by account and other dimensions., , , ]

Question 21

A company's solutions architect is reviewing a new internally developed application in a sandbox AWS account The application uses an AWS Auto Scaling group of Amazon EC2 instances that have an IAM instance profile attached Part of the application logic creates and accesses secrets from AWS Secrets Manager The company has an AWS Lambda function that calls the application API to test the functionality The company also has created an AWS CloudTrail trail in the account

The application's developer has attached the SecretsManagerReadWnte AWS managed IAM policy to an IAM role The IAM role is associated with the instance profile that is attached to the EC2 instances The solutions architect has invoked the Lambda function for testing

The solutions architect must replace the SecretsManagerReadWnte policy with a new policy that provides least privilege access to the Secrets Manager actions that the application requires

What is the MOST operationally efficient solution that meets these requirements?

Options:

Generate a policy based on CloudTrail events for the IAM role Use the generated policy output to create a new IAM policy Use the newly generated IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role

Create an analyzer in AWS Identity and Access Management Access Analyzer Use the IAM role's Access Advisor findings to create a new IAM policy Use the newly created IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role

Use the aws cloudtrail lookup-events AWS CLI command to filter and export CloudTrail events that are related to Secrets Manager Use a new IAM policy that contains the actions from CloudTrail to replace the SecretsManagerReadWnte policy that is attached to the IAM role

Use the IAM policy simulator to generate an IAM policy for the IAM role Use the newly generated IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role

Discussion

Question 22

A solutions architect is redesigning a three-tier application that a company hosts onpremises. The application provides personalized recommendations based on user profiles. The company already has an AWS account and has configured a VPC to host the application.

The frontend is a Java-based application that runs in on-premises VMs. The company hosts a personalization model on a physical application server and uses TensorFlow to implement the model. The personalization model uses artificial intelligence and machine learning (AI/ML). The company stores user information in a Microsoft SQL Server database. The web application calls the personalization model, which reads the user profiles from the database and provides recommendations.

The company wants to migrate the redesigned application to AWS.

Which solution will meet this requirement with the LEAST operational overhead?

Options:

Use AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Export the personalization model. Store the model artifacts in Amazon S3. Deploy the model to Amazon SageMaker and create an endpoint. Host the Java application in AWS Elastic Beanstalk. Use AWS Database Migration Service {AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Use AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to an EC2 instance.

Containerize the personalization model and the Java application. Use Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy the model and the application to Amazon EKS Host the node groups in a VPC. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Discussion

Answer:

Explanation:

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.

Question 23

A company has multiple business units that each have separate accounts on AWS. Each business unit manages its own network with several VPCs that have CIDR ranges that overlap. The company’s marketing team has created a new internal application and wants to make the application accessible to all the other business units. The solution must use private IP addresses only.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Instruct each business unit to add a unique secondary CIDR range to the business unit's VPC. Peer the VPCs and use a private NAT gateway in the secondary range to route traffic to the marketing team.

Create an Amazon EC2 instance to serve as a virtual appliance in the marketing account's VPC. Create an AWS Site-to-Site VPN connection between the marketing team and each business unit's VPC. Perform NAT where necessary.

Create an AWS PrivateLink endpoint service to share the marketing application. Grant permission to specific AWS accounts to connect to the service. Create interface VPC endpoints in other accounts to access the application by using private IP addresses.

Create a Network Load Balancer (NLB) in front of the marketing application in a private subnet. Create an API Gateway API. Use the Amazon API Gateway private integration to connect the API to the NLB. Activate IAM authorization for the API. Grant access to the accounts of the other business units.