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NEW QUESTION: 1
アプリケーションは、複数のアベイラビリティーゾーンにまたがるAmazon EC2インスタンスで実行されます。インスタンスは、アプリケーションロードバランサーの背後にあるAmazon EC2 Auto Scalingグループで実行されます。アプリケーションは、EC2インスタンスのCPU使用率が40％以下のときに最高のパフォーマンスを発揮します。グループ内のすべてのインスタンスで望ましいパフォーマンスを維持しますか？
A. 単純なスケーリングポリシーを使用してAuto Scalingグループを動的にスケーリングします
B. AWS Lambda関数を使用して、目的のAuto Scalingグループ容量を更新します
C. ターゲットトラッキングポリシーを使用して、Auto Scalingグループを動的にスケーリングします
D. スケジュールされたスケーリングアクションを使用して、Auto Scalingグループを拡大および縮小します
Answer: C
Explanation:
Reference:
"With target tracking scaling policies, you select a scaling metric and set a target value. Amazon EC2 Auto Scaling creates and manages the CloudWatch alarms that trigger the scaling policy and calculates the scaling adjustment based on the metric and the target value. The scaling policy adds or removes capacity as required to keep the metric at, or close to, the specified target value. In addition to keeping the metric close to the target value, a target tracking scaling policy also adjusts to changes in the metric due to a changing load pattern. For example, you can use target tracking scaling to: Configure a target tracking scaling policy to keep the average aggregate CPU utilization of your Auto Scaling group at 40 percent. Configure a target tracking scaling policy to keep the request count per target of your Application Load Balancer target group at 1000 for your Auto Scaling group."

NEW QUESTION: 2

A. The quantity in Column B is greater;
B. The quantities are equal;
D .The relationship cannot be determined from the information given.
C. The quantity in Column A is greater;
Answer: B

NEW QUESTION: 3
会社は、Amazon S3の外部プロバイダーからのデータセットを受け取ります。異なるプロバイダーからのデータセットは互いに依存しています。データセットは異なる速度で駆動され、特定の順序ではありません。
データアーキテクトは、会社が次のことを行えるようにするソリューションを設計する必要があります。
*データが利用可能になり次第、クロスデータセット分析を迅速に実行
*異なる時期に到着するデータセット間の依存関係を管理するどのアーキテクチャ戦略が、これらの要件を満たすスケーラブルで費用対効果の高いソリューションを提供しますか？
A. Amazon DynamoDBテーブルのデータ依存情報を維持します。 Amazon S3イベント通知を使用して、S3オブジェクトをDynamoDBで関連付けられたタスクにマッピングするAWS Lambda関数をトリガーします。すべてのタスクの依存関係が解決されたら、Amazon EMRでデータを処理します
B. Amazon RDS for MySQLでデータ依存情報を維持します。 AWS Pipelineジョブを使用して、Amazon S3のタスク依存関係とイベント通知トリガーに基づいてAmazon EMR Hiveテーブルをロードします
C. Amazon ElasticCache Redisクラスターでデータ依存情報を維持します。 Amazon S3イベント通知を使用して、S3オブジェクトをRedisにマッピングするAWS Lambda関数をトリガーします。依存関係が解決されたら、Amazon EMRでデータを処理します
D. Amazon DynamoDBテーブルのデータ依存情報を維持します。 Amazon SNSとイベント通知を使用して、Amazon EC2ワーカーの艦隊にデータを発行します。タスクの依存関係が解決されたら、Amazon EMRでデータを処理します
Answer: C