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NEW QUESTION: 1
国際企業は、単一地域でDynamoDBに依存する多層Webアプリケーションを展開しています。規制上の理由により、2時間の目標復旧時間と24時間の目標復旧ポイントを備えた個別の地域の災害復旧機能が必要です。定期的にデータを同期し、CloudFormationを使用してWebアプリケーションを迅速にプロビジョニングできる必要があります。
目的は、既存のWebアプリケーションへの変更を最小限に抑え、データの同期に使用されるDynamoDBのスループットを制御し、変更された要素のみを同期することです。
これらの要件を満たすために、どの設計を選択しますか？
A. AWS Data Pipelineを使用して、1日1回DynamoDBクロスリージョンコピーをスケジュールし、
最後の更新のタイムスタンプを表し、それをフィルターとして使用するDynamoDBテーブルの「LastUpdated」属性
B. EMRを使用してカスタムスクリプトを記述し、SCAN操作を使用して現在のリージョンのDynamoDBからデータを取得し、2番目のリージョンのDynamoDBにプッシュします
C. 各書き込みも2番目の領域のSQSキューに送信し、SQSキューの背後で自動スケーリンググループを使用して2番目の領域で書き込みを再生します
D. AWS Data Pipelineを使用して、現在のリージョンのDynamoDBテーブルのS3へのエクスポートを1日に1回スケジュールし、その後すぐに別のタスクをスケジュールして、S3から他のリージョンのDynamoDBにデータをインポートします
Answer: D
Explanation:
dynamo DBのエクスポートは増分であり、最新の変更でバックアップを修正します。
NEW QUESTION: 2
회사는 여러 웹 사이트에서 클릭 스트림 데이터를 캡처하고 일괄 처리를 사용하여 분석합니다. 데이터는 매일 밤 Amazon Redshift에로드되며 비즈니스 분석가가 사용합니다. 이 회사는시기 적절한 통찰력을 얻기 위해 거의 실시간에 가까운 데이터 처리를 원합니다. 솔루션은 최소한의 노력과 운영 오버 헤드로 스트리밍 데이터를 처리해야 합니다.
이 솔루션에 가장 비용 효율적인 AWS 서비스 조합은 무엇입니까? (2 개를 선택하십시오.)
A. Amazon Kinesis Data Firehose
B. AWS Lambda
C. Amazon Kinesis 데이터 스트림
D. 아마존 EC2
E. Amazon Kinesis Data Analytics
Answer: C,E
Explanation:
Explanation
Kinesis Data Streams and Kinesis Client Library (KCL) - Data from the data source can be continuously captured and streamed in near real-time using Kinesis Data Streams.
With the Kinesis Client Library (KCL), you can build your own application that can preprocess the streaming data as they arrive and emit the data for generating incremental views and downstream analysis.
Kinesis Data Analytics - This service provides the easiest way to process the data that is streaming through Kinesis Data Stream or Kinesis Data Firehose using SQL. This enables customers to gain actionable insight in near real-time from the incremental stream before storing it in Amazon S3.
https://d1.awsstatic.com/whitepapers/lambda-architecure-on-for-batch-aws.pdf
NEW QUESTION: 3
会社は、AWSマネージドサービスを使用して構築されたソリューションにコールシステムを置き換えることを望んでいます。同社のコールセンターは、このソリューションが電話を受け、コンタクトフローを作成し、成長予測を処理するように拡張することを望んでいます。また、コールセンターでは、ディープラーニング機能を使用して発信者の意図を認識し、基本的なタスクを処理して、エージェントと話す必要性を減らすソリューションも求めています。このソリューションは、ビジネスアプリケーションにクエリを実行し、要求に応じて関連情報を呼び出しに提供できる必要もあります。
ソリューションアーキテクトは、このソリューションを構築するためにどのサービスを使用する必要がありますか？ （3つ選択してください。）
A. 内部システムと統合するAWS Lambda。
B. 発信者の意図を認識するAmazon Lex。
C. 会話型インターフェースを構築するためのAmazon Alexa for Business。
D. 呼び出しているIDに対するAmazon Rekognition。
E. クラウドベースのコンタクトセンターを作成するためのAmazon Connect。
F. 着信発信者をキューに追加するAmazon SQS。
Answer: A,E,F