A company has migrated an application to Amazon EC2 Linux instances. One of these EC2 instances runs several 1-hour tasks on a schedule. These tasks were written by different teams and have no common programming language. The company is concerned about performance and scalability while these tasks run on a single instance. A solutions architect needs to implement a solution to resolve these concerns.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use AWS Batch to run the tasks as jobs. Schedule the jobs by using Amazon EventBridge (Amazon CloudWatch Events).
• B.Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs.
• C.Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events).
• D.Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance.
• E.Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance. This solution will not meet the requirement of low operational overhead, as it involves creating and maintaining AMIs and Auto Scaling groups, which are additional resources that need to be configured and managed2.

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At part of budget planning. management wants a report of AWS billed dams listed by user. The data will be used to create department budgets. A solution architect needs to determine the most efficient way to obtain this report Information Which solution meets these requirement?

• A.Run a query with Amazon Athena to generate the report.
• B.Modify a cost budget in AWS Budgets to alert with Amazon Simple Email Service (Amazon SES).
• C.Create a report in Cost Explorer and download the report
• D.Access the bill details from the runing dashboard and download Via bill.

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A company has a highly dynamic batch processing job that uses many Amazon EC2 instances to complete it. The job is stateless in nature, can be started and stopped at any given time with no negative impact, and typically takes upwards of 60 minutes total to complete. The company has asked a solutions architect to design a scalable and cost-effective solution that meets the requirements of the job.
What should the solutions architect recommend?

• A.Implement EC2 Spot Instances
• B.Purchase EC2 Reserved Instances
• C.Implement EC2 On-Demand Instances
• D.Implement the processing on AWS Lambda

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A company wants to host a scalable web application on AWS. The application will be accessed by users from different geographic regions of the world. Application users will be able to download and upload unique data up to gigabytes in size. The development team wants a cost-effective solution to minimize upload and download latency and maximize performance.
What should a solutions architect do to accomplish this?

• A.Use Amazon S3 with Transfer Acceleration to host the application.
• B.Use Amazon S3 with CacheControl headers to host the application.
• C.Use Amazon EC2 with Auto Scaling and Amazon CloudFront to host the application.
• D.Use Amazon EC2 with Auto Scaling and Amazon ElastiCache to host the application.

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An ecommerce company stores terabytes of customer data in the AWS Cloud. The data contains personally identifiable information (Pll). The company wants to use the data in three applications. Only one of the applications needs to process the Pll. The Pll must be removed before the other two applications process the data.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Store the data in an Amazon DynamoDB table. Create a proxy application layer to intercept and process the data that each application requests.
• B.Store the data in an Amazon S3 bucket. Process and transform the data by using S3 Object Lambda before returning the data to the requesting application.
• C.Process the data and store the transformed data in three separate Amazon S3 buckets so that each application has its own custom dataset. Point each application to its respective S3 bucket.
• D.Process the data and store the transformed data in three separate Amazon DynamoDB tables so that each application has its own custom dataset. Point each application to its respective DynamoDB table.

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