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Free AWS-SOLUTIONS-ARCHITECT-PROFESSIONAL Exam Braindumps (page: 53)

Page 53 of 134
PDF with 528 Questions

A company wants to migrate to AWS. The company is running thousands of VMs in a VMware ESXi environment. The company has no configuration management database and has little knowledge about the utilization of the VMware portfolio.

A solutions architect must provide the company with an accurate inventory so that the company can plan for a cost-effective migration.

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

  1. Use AWS Systems Manager Patch Manager to deploy Migration Evaluator to each VM. Review the collected data in Amazon QuickSight. Identify servers that have high utilization. Remove the servers that have high utilization from the migration list. Import the data to AWS Migration Hub.
  2. Export the VMware portfolio to a .csv file. Check the disk utilization for each server. Remove servers that have high utilization. Export the data to AWS Application Migration Service. Use AWS Server Migration Service (AWS SMS) to migrate the remaining servers.
  3. Deploy the Migration Evaluator agentless collector to the ESXi hypervisor. Review the collected data in Migration Evaluator. Identify inactive servers. Remove the inactive servers from the migration list. Import the data to AWS Migration Hub.
  4. Deploy the AWS Application Migration Service Agent to each VM. When the data is collected, use Amazon Redshift to import and analyze the data. Use Amazon QuickSight for data visualization.

Answer(s): C

Explanation:

C: Deploying the Migration Evaluator agentless collector to the ESXi hypervisor provides an automated and low-overhead way to gather inventory and utilization data without needing to install agents on each VM. This method allows for efficient data collection and analysis of the VMware environment. Reviewing the collected data helps identify inactive or underutilized servers for cost-effective migration planning, making this solution the most efficient with minimal operational overhead.



A company runs a microservice as an AWS Lambda function. The microservice writes data to an on-premises SQL database that supports a limited number of concurrent connections. When the number of Lambda function invocations is too high, the database crashes and causes application downtime. The company has an AWS Direct Connect connection between the company's VPC and the on-premises data center. The company wants to protect the database from crashes.

Which solution will meet these requirements?

  1. Write the data to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to read from the queue and write to the existing database. Set a reserved concurrency limit on the Lambda function that is less than the number of connections that the database supports.
  2. Create a new Amazon Aurora Serverless DB cluster. Use AWS DataSync to migrate the data from the existing database to Aurora Serverless. Reconfigure the Lambda function to write to Aurora.
  3. Create an Amazon RDS Proxy DB instance. Attach the RDS Proxy DB instance to the Amazon RDS DB instance. Reconfigure the Lambda function to write to the RDS Proxy DB instance.
  4. Write the data to an Amazon Simple Notification Service (Amazon SNS) topic. Invoke the Lambda function to write to the existing database when the topic receives new messages. Configure provisioned concurrency for the Lambda function to be equal to the number of connections that the database supports.

Answer(s): A

Explanation:

A: Writing data to Amazon SQS and setting a reserved concurrency limit on the Lambda function prevents overloading the database by controlling the number of simultaneous database connections. The queue buffers incoming data and allows the Lambda function to process it at a rate that the database can handle, preventing crashes and downtime, while also using AWS's managed service features efficiently.



A company uses a Grafana data visualization solution that runs on a single Amazon EC2 instance to monitor the health of the company's AWS workloads. The company has invested time and effort to create dashboards that the company wants to preserve. The dashboards need to be highly available and cannot be down for longer than 10 minutes. The company needs to minimize ongoing maintenance.

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

  1. Migrate to Amazon CloudWatch dashboards. Recreate the dashboards to match the existing Grafana dashboards. Use automatic dashboards where possible.
  2. Create an Amazon Managed Grafana workspace. Configure a new Amazon CloudWatch data source. Export dashboards from the existing Grafana instance. Import the dashboards into the new workspace.
  3. Create an AMI that has Grafana pre-installed. Store the existing dashboards in Amazon Elastic File System (Amazon EFS). Create an Auto Scaling group that uses the new AMI. Set the Auto Scaling group's minimum, desired, and maximum number of instances to one. Create an Application Load Balancer that serves at least two Availability Zones.
  4. Configure AWS Backup to back up the EC2 instance that runs Grafana once each hour. Restore the EC2 instance from the most recent snapshot in an alternate Availability Zone when required.

Answer(s): B

Explanation:

B: Using Amazon Managed Grafana minimizes operational overhead as it is a fully managed service. It ensures high availability and provides seamless integration with AWS data sources like CloudWatch. By exporting and importing the existing dashboards, the company can preserve its investment in creating them, and Managed Grafana will handle maintenance and scaling, meeting the requirement of less than 10 minutes of downtime.



A company needs to migrate its customer transactions database from on premises to AWS. The database resides on an Oracle DB instance that runs on a Linux server. According to a new security requirement, the company must rotate the database password each year.

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

  1. Convert the database to Amazon DynamoDB by using the AWS Schema Conversion Tool (AWS SCT). Store the password in AWS Systems Manager Parameter Store. Create an Amazon CloudWatch alarm to invoke an AWS Lambda function for yearly passtard rotation.
  2. Migrate the database to Amazon RDS for Oracle. Store the password in AWS Secrets Manager. Turn on automatic rotation. Configure a yearly rotation schedule.
  3. Migrate the database to an Amazon EC2 instance. Use AWS Systems Manager Parameter Store to keep and rotate the connection string by using an AWS Lambda function on a yearly schedule.
  4. Migrate the database to Amazon Neptune by using the AWS Schema Conversion Tool (AWS SCT). Create an Amazon CloudWatch alarm to invoke an AWS Lambda function for yearly password rotation.

Answer(s): B

Explanation:

B: Migrating the database to Amazon RDS for Oracle and using AWS Secrets Manager with automatic password rotation provides the least operational overhead. Secrets Manager handles secure storage and rotation of database credentials, and configuring a yearly rotation schedule meets the security requirement without manual intervention, making this solution both secure and low-maintenance.



Page 53 of 134



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