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NEW QUESTION: 1
A company's production application runs online transaction processing (OLTP) transactions on an Amazon RDS MySQL DB instance The company is launching a new reporting tool that will access the same data The reporting tool must be highly available and not impact the performance of the production application.
How can this be achieved?
A. Create a Single-AZ RDS Read Replica of the production RDS DB instance Create a second Single-AZ RDS Read Replica from the replica
B. Create hourly snapshots of the production RDS DB instance
C. Create multiple RDS Read Replicas of the production RDS DB instance Place the Read Replicas in an Auto Scaling group
D. Create a Multi-AZ RDS Read Replica of the production RDS DB instance
Amazon RDS Read Replicas Now Support Multi-AZ Deployments
Amazon RDS Read Replicas enable you to create one or more read-only copies of your database instance within the same AWS Region or in a different AWS Region. Updates made to the source database are then asynchronously copied to your Read Replicas. In addition to providing scalability for read-heavy workloads, Read Replicas can be promoted to become a standalone database instance when needed.
Amazon RDS Multi-AZ deployments provide enhanced availability for database instances within a single AWS Region. With Multi-AZ, your data is synchronously replicated to a standby in a different Availability Zone (AZ). In the event of an infrastructure failure, Amazon RDS performs an automatic failover to the standby, minimizing disruption to your applications.
You can now use Read Replicas with Multi-AZ as part of a disaster recovery (DR) strategy for your production databases. A well-designed and tested DR plan is critical for maintaining business continuity after a disaster. A Read Replica in a different region than the source database can be used as a standby database and promoted to become the new production database in case of a regional disruption.
NEW QUESTION: 2
A. 処理が必要なジョブを送信するためのAmazon SNSトピックを作成しますプロセッサーアプリケーションで構成されるAmazonマシンイメージ（AMI）を作成しますAMIを使用する起動テンプレートを作成します起動テンプレートセットを使用してAuto Scalingグループを作成しますSNSトピックにパブリッシュされたメッセージの数に基づいてノードを追加および削除するAuto Scalingグループのスケーリングポリシー。
B. 処理が必要なジョブを送信するためのAmazon SNSトピックを作成しますプロセッサーアプリケーションで構成されるAmazonマシンイメージ（AMI）を作成しますAMIを使用する起動構成を作成します起動構成セットを使用してAuto Scalingグループを作成しますCPU使用率に基づいてノードを追加および削除するAuto Scalingグループのスケーリングポリシー
C. 処理が必要なジョブを保持するAmazon SQSキューを作成しますプロセッサーアプリケーションで構成されるAmazonマシンイメージ（AMI）を作成しますAMIを使用する起動構成を作成します起動構成セットを使用してAuto Scalingグループを作成しますネットワークの使用状況に基づいてノードを追加および削除するAuto Scalingグループのスケーリングポリシー
D. 処理が必要なジョブを保持するAmazon SQSキューを作成します。プロセッサーアプリケーションで構成されるAmazonマシンイメージ（AMI）を作成します。AMIを使用する起動テンプレートを作成します。起動テンプレートを使用してAuto Scalingグループを作成します。 SQSキュー内のアイテム数に基づいてノードを追加および削除するAuto Scalingグループのスケーリングポリシー
Amazon Simple Queue Service
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands.
SQS offers two types of message queues. Standard queues offer maximum throughput, best-effort ordering, and at-least-once delivery. SQS FIFO queues are designed to guarantee that messages are processed exactly once, in the exact order that they are sent.
Scaling Based on Amazon SQS
There are some scenarios where you might think about scaling in response to activity in an Amazon SQS queue. For example, suppose that you have a web app that lets users upload images and use them online. In this scenario, each image requires resizing and encoding before it can be published. The app runs on EC2 instances in an Auto Scaling group, and it's configured to handle your typical upload rates. Unhealthy instances are terminated and replaced to maintain current instance levels at all times. The app places the raw bitmap data of the images in an SQS queue for processing. It processes the images and then publishes the processed images where they can be viewed by users. The architecture for this scenario works well if the number of image uploads doesn't vary over time. But if the number of uploads changes over time, you might consider using dynamic scaling to scale the capacity of your Auto Scaling group.