QUESTION: 297

A company's solutions architect is evaluating an AWS workload that was deployed several years ago. The application tier is stateless and runs on a single large Amazon EC2 instance that was launched from an AMI. The application stores data in a MySQL database that runs on a single EC2 instance.

The CPU utilization on the application server EC2 instance often reaches 100% and causes the application to stop responding. The company manually installs patches on the instances. Patching has caused downtime in the past. The company needs to make the application highly available.

Which solution will meet these requirements with the LEAST development me?

Move the application tier to AWS Lambda functions in the existing VPC. Create an Application Load Balancer to distribute traffic across the Lambda functions. Use Amazon GuardDuty to scan the Lambda functions. Migrate the database to Amazon DocumentDB (with MongoDB compatibility.
Change the EC2 instance type to a smaller Graviton powered instance type. Use the existing AMI to create a launch template for an Auto Scaling group. Create an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Set the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon DynamoD
Move the application tier to containers by using Docker. Run the containers on Amazon Elastic Container Service (Amazon ECS) with EC2 instances. Create an Application Load Balancer to distribute traffic across the ECS cluster. Configure the ECS cluster to scale based on CPU utilization. Migrate the database to Amazon Neptune.
Create a now AMI that is configured with AWS Systems Manager Agent (SSM Agent). Use the new AMI to create a launch template for an Auto Scaling group. Use smaller instances in the Auto Scaling group. Create an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Set the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon Aurora MySQL.

Answer(s): D

Explanation:

D) is the best solution because it meets the requirements with minimal development effort while making the application highly available.
-AMI with Systems Manager Agent (SSM Agent): This allows for automated patch management, which eliminates the need for manual patching and reduces downtime due to maintenance.
-Auto Scaling Group with smaller instances: This helps distribute traffic across multiple instances, providing high availability and scaling based on CPU utilization to handle increased loads, addressing the CPU saturation issue.
-Application Load Balancer: This distributes incoming traffic evenly across the Auto Scaling group, ensuring that the application remains responsive.
-Amazon Aurora MySQL: Migrating the database to Amazon Aurora MySQL provides a managed database solution that is highly available, with built-in automatic backups, replication, and failover.
This approach ensures high availability, scalability, and reduces operational overhead without significant re-architecting of the application.

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QUESTION: 298

A company is planning to migrate several applications to AWS. The company does not have a good understanding of its entire application estate. The estate consists of a mixture of physical machines and VMs.

One application that the company will migrate has many dependencies that are sensitive to latency. The company is unsure what all the dependencies are. However the company knows that the low-latency communications use a custom IP-based protocol that runs on port 1000. The company wants to migrate the application and these dependencies together to move all the low-latency interfaces to AWS at the same time.

The company has installed the AWS Application Discovery Agent and has been collecting data for several months.

What should the company do to identify the dependencies that need to be migrated in the same phase as the application?

Use AWS Migration Hub and select the servers that host the application. Visualize the network graph to find servers that interact with the application. Turn on data exploration in Amazon Athena. Query the data that is transferred between the servers to identify the servers that communicate on port 1000. Return to Migration Hub. Create a move group that is based on the findings from the Athena queries.
Use AWS Application Migration Service and select the servers that host the application. Visualize the network graph to find servers that interact with the application. Configure Application Migration Service to launch test instances for all the servers that interact with the application. Perform acceptance tests on the test instances. If no issues are identified, create a move group that is based on the tested servers.
Use AWS Migration Hub and select the servers that host the application. Turn on data exploration in Network Access Analyzer. Use the Network Access Analyzer console to select the servers that host the application. Select a Network Access Scope of port 1000 and note the matching servers. Return to Migration Hub. Create a move group that is based on the findings from Network Access Analyzer.
Use AWS Migration Hub and select the servers that host the application. Push the Amazon CloudWalch agent to the identified servers by using the AWS Application Discovery Agent. Export the CloudWatch logs that the agents collect to Amazon S3. Use Amazon Athena to query the logs to find servers that communicate on port 1000. Return to Migration Hub Create a move group that is based on the findings from the Athena queries.

Answer(s): A

Explanation:

A) is the best solution because AWS Migration Hub, along with the AWS Application Discovery Agent, can visualize network dependencies and identify servers that interact with the application.
-Visualizing the network graph in Migration Hub: This allows the company to quickly identify all the servers that communicate with the application.
-Using Amazon Athena to query the data: By querying the data and filtering for port 1000, the company can find all dependencies using the custom IP-based protocol that requires low-latency communication.
-After identifying the necessary servers, creating a move group in Migration Hub helps to ensure that all related dependencies are migrated together with the application.
This approach minimizes latency risks by grouping and migrating dependent systems together, providing the company with a more seamless migration of low-latency interfaces.

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QUESTION: 299

A company is building an application that will run on an AWS Lambda function. Hundreds of customers will use the application. The company wants to give each customer a quota of requests for a specific time period. The quotas must match customer usage patterns. Some customers must receive a higher quota for a shorter time period.

Which solution will meet these requirements?

Create an Amazon API Gateway REST API with a proxy integration to invoke the Lambda function. For each customer, configure an API Gateway usage plan that includes an appropriate request quota. Create an API key from the usage plan for each user that the customer needs.
Create an Amazon API Gateway HTTP API with a proxy integration to invoke the Lambda function. For each customer configure an API Gateway usage plan that includes an appropriate request quota Configure route-level throttling for each usage plan. Create an API Key from the usage plan for each user that the customer needs.
Create a Lambda function alias for each customer. Include a concurrency limit with an appropriate request quota. Create a Lambda function URL for each function alias. Share the Lambda function URL for each alias with the relevant customer.
Create an Application Load Balancer (ALB) in a VPC. Configure the Lambda function as a target for the ALB. Configure an AWS WAF web ACL for the ALB. For each customer configure a rale-based rule that includes an appropriate request quota.

Answer(s): A

Explanation:

A) is the most appropriate solution because Amazon API Gateway provides a built-in mechanism for managing quotas, rate limiting, and access control. Here’s how this solution meets the requirements:
-API Gateway usage plans allow for setting request quotas and throttling limits, which can be configured per customer to match their usage patterns. Each customer can have different quotas for specific time periods, enabling flexibility in the allocation of resources.
-API keys can be assigned to individual customers, ensuring that each customer's requests are tracked and limited according to their quota.
-API Gateway provides seamless integration with AWS Lambda, allowing the application to scale efficiently while adhering to customer-specific quotas.
This solution is simple to implement and directly addresses the need for customer-specific request quotas.

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QUESTION: 300

A company is planning to migrate its on-premises VMware cluster of 120 VMs to AWS. The VMs have many different operating systems and many custom software packages installed. The company also has an on-premises NFS server that is 10 TB in size. The company has set up a 10 Gbps AWS Direct Connect connection to AWS for the migration.

Which solution will complete the migration to AWS in the LEAST amount of time?

Export the on-premises VMs and copy them to an Amazon S3 bucket. Use VM Import/Export to create AMIs from the VM images that are stored in Amazon S3. Order an AWS Snowball Edge device. Copy the NFS server data to the device. Restore the NFS server data to an Amazon EC2 instance that has NFS configured.
Configure AWS Application Migration Service with a connection to the VMware cluster. Create a replication job for the VMS. Create an Amazon Elastic File System (Amazon EFS) file system. Configure AWS DataSync to copy the NFS server data to the EFS file system over the Direct Connect connection.
Recreate the VMs on AWS as Amazon EC2 instances. Install all the required software packages. Create an Amazon FSx for Lustre file system. Configure AWS DataSync to copy the NFS server data to the FSx for Lustre file system over the Direct Connect connection.
Order two AWS Snowball Edge devices. Copy the VMs and the NFS server data to the devices. Run VM Import/Export after the data from the devices is loaded to an Amazon S3 bucket. Create an Amazon Elastic File System (Amazon EFS) file system. Copy the NFS server data from Amazon S3 to the EFS file system.

Answer(s): B

Explanation:

B) is the best solution as it leverages AWS Application Migration Service (AWS MGN) and AWS DataSync to minimize the migration time, especially with a high-speed Direct Connect connection already set up. Here's how this solution works efficiently:
-AWS Application Migration Service (MGN) provides a streamlined and automated way to migrate VMware-based VMs to AWS with minimal downtime. It replicates live VM data continuously to AWS, ensuring a quick and consistent cutover.
-AWS DataSync is optimized for data transfer and can handle the migration of large datasets (like the 10 TB NFS server data) efficiently over the 10 Gbps Direct Connect connection. Using DataSync ensures that data transfer is automated and does not require manual intervention.
This combination will provide the quickest migration path while reducing operational overhead and ensuring minimal disruption to services. The other options either rely on slower methods (like VM Import/Export or manual setup) or introduce additional complexities and delays.

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QUESTION: 297

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QUESTION: 298

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QUESTION: 299

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QUESTION: 300

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