QUESTION: 93

A company has migrated Its forms-processing application to AWS. When users interact with the application, they upload scanned forms as files through a web application. A database stores user metadata and references to files that are stored in Amazon S3. The web application runs on Amazon EC2 instances and an Amazon RDS for PostgreSQL database.

When forms are uploaded, the application sends notifications to a team through Amazon Simple Notification Service (Amazon SNS). A team member then logs in and processes each form. The team member performs data validation on the form and extracts relevant data before entering the information into another system that uses an API.

A solutions architect needs to automate the manual processing of the forms. The solution must provide accurate form extraction. minimize time to market, and minimize tong-term operational overhead.

Which solution will meet these requirements?

Develop custom libraries to perform optical character recognition (OCR) on the forms. Deploy the libraries to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster as an application tier. Use this tier to process the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data into an Amazon DynamoDB table. Submit the data to the target system's APL. Host the new application tier on EC2 instances.
Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use artificial intelligence and machine learning (AI/ML) models that are trained and hosted on an EC2 instance to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine teaming (AI/ML) models that are trained and hosted in Amazon SageMaker to perform optical character recognition (OCR) on the forms. Store the output in Amazon ElastiCache. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

Answer(s): D

Explanation:

D) Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

This solution leverages Amazon Textract for OCR and Amazon Comprehend for extracting relevant information, both of which are fully managed services that reduce operational overhead and ensure high accuracy. Using AWS Step Functions and AWS Lambda provides a serverless, low-maintenance, and scalable solution for automating the forms processing workflow. This solution minimizes time to market by utilizing AWS AI/ML services, which don't require custom development, and provides automation with minimal long-term operational overhead.

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QUESTION: 94

A company is refactoring its on-premises order-processing platform in the AWS Cloud. The platform includes a web front end that is hosted on a fleet of VMs, RabbitMQ to connect the front end to the backend, and a Kubernetes cluster to run a containerized backend system to process the orders. The company does not want to make any major changes to the application.

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

Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
Create a custom AWS Lambda runtime to mimic the web server environment. Create an Amazon API Gateway API to replace the front-end web servers. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Install Kubernetes on a fleet of different EC2 instances to host the order-processing backend.
Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up an Amazon Simple Queue Service (Amazon SQS) queue to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.

Answer(s): A

Explanation:

A) Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.

This solution has the least operational overhead because it reuses the existing architecture (web front end, messaging queue, Kubernetes-based backend) with minimal changes while migrating to AWS. It leverages Amazon MQ as a managed service for RabbitMQ, reducing management overhead for the messaging layer. Amazon EKS is used to manage the containerized backend, which is similar to the on-premises Kubernetes setup, ensuring compatibility. Using EC2 Auto Scaling and an Application Load Balancer provides scalability for the web front end without requiring major changes.

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QUESTION: 95

A company has developed a web application. The company is hosting the application on a group of Amazon EC2 instances behind an Application Load Balancer. The company wants to improve the security posture of the application and plans to use AWS WAF web ACLs. The solution must not adversely affect legitimate traffic to the application.

How should a solutions architect configure the web ACLs to meet these requirements?

Set the action of the web ACL rules to Count. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Count to Block.
Use only rate-based rules in the web ACLs, and set the throttle limit as high as possible. Temporarily block all requests that exceed the limit. Define nested rules to narrow the scope of the rate tracking.
Set the action of the web ACL rules to Block. Use only AWS managed rule groups in the web ACLs. Evaluate the rule groups by using Amazon CloudWatch metrics with AWS WAF sampled requests or AWS WAF logs.
Use only custom rule groups in the web ACLs, and set the action to Allow. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Allow to Block.

Answer(s): A

Explanation:

A) Set the action of the web ACL rules to Count. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Count to Block.

Starting with the Count action allows you to evaluate traffic without blocking legitimate requests. By enabling AWS WAF logging, you can analyze the traffic patterns and false positives to fine-tune the rules before changing them to Block. This approach ensures that the security posture is improved without affecting legitimate traffic, gradually increasing protection over time.

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QUESTION: 96

A company has an organization that has many AWS accounts in AWS Organizations. A solutions architect must improve how the company manages common security group rules for the AWS accounts in the organization.

The company has a common set of IP CIDR ranges in an allow list in each AWS account to allow access to and from the company’s on-premises network. Developers within each account are responsible for adding new IP CIDR ranges to their security groups. The security team has its own AWS account. Currently, the security team notifies the owners of the other AWS accounts when changes are made to the allow list.

The solutions architect must design a solution that distributes the common set of CIDR ranges across all accounts.

Which solution meets these requirements with the LEAST amount of operational overhead?

Set up an Amazon Simple Notification Service (Amazon SNS) topic in the security team's AWS account. Deploy an AWS Lambda function in each AWS account. Configure the Lambda function to run every time an SNS topic receives a message. Configure the Lambda function to take an IP address as input and add it to a list of security groups in the account. Instruct the security team to distribute changes by publishing messages to its SNS topic.
Create new customer-managed prefix lists in each AWS account within the organization. Populate the prefix lists in each account with all internal CIDR ranges. Notify the owner of each AWS account to allow the new customer-managed prefix list IDs in their accounts in their security groups. Instruct the security team to share updates with each AWS account owner.
Create a new customer-managed prefix list in the security team’s AWS account. Populate the customer-managed prefix list with all internal CIDR ranges. Share the customer-managed prefix list with the organization by using AWS Resource Access Manager. Notify the owner of each AWS account to allow the new customer-managed prefix list ID in their security groups.
Create an IAM role in each account in the organization. Grant permissions to update security groups. Deploy an AWS Lambda function in the security team’s AWS account. Configure the Lambda function to take a list of internal IP addresses as input, assume a role in each organization account, and add the list of IP addresses to the security groups in each account.

Answer(s): C

Explanation:

C) Create a new customer-managed prefix list in the security team’s AWS account. Populate the customer-managed prefix list with all internal CIDR ranges. Share the customer-managed prefix list with the organization by using AWS Resource Access Manager. Notify the owner of each AWS account to allow the new customer-managed prefix list ID in their security groups.

This solution effectively consolidates the management of IP CIDR ranges by using customer-managed prefix lists, which allow for easy updates and sharing across accounts. By sharing the prefix list through AWS Resource Access Manager, it reduces operational overhead as the security team can manage the CIDR ranges centrally, and other accounts can reference the same list without needing individual updates. This approach simplifies security management while ensuring all accounts are using a consistent allow list.

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