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NEW QUESTION: 1
開発チームは現在、AWS CodeDeployを使用してAuto Scalingグループにアプリケーションリビジョンをデプロイしています。展開プロセスが失敗した場合、自動的にロールバックし、通知を送信する必要があります。
すべての要件を満たすことができるMOSTの効果的な構成とは何ですか？
A. AWS CloudTrailを使用して、AWSアカウントのCodeDeployによって、またはその代わりに行われたAPI呼び出しを監視します。
デプロイが失敗したときにAmazon SNSメッセージを送信します。 AWS CLIを使用して、以前にデプロイされたリビジョンを再デプロイします。
B. CodeDeployで利用可能なAmazon CloudWatchメトリックスを使用して、CloudWatchアラームを作成します。デプロイが失敗したときにAmazon SNSメッセージを送信するようにCloudWatchアラームを設定します。 AWS CLIを使用して、以前にデプロイされたリビジョンを再デプロイします。
C. CodeDeployオペレーションのAmazon CloudWatch Eventsルールを作成します。デプロイが失敗したときにAmazon SNSメッセージを送信するようにCloudWatchイベントルールを設定します。展開が失敗したときに自動的にロールバックするようにCodeDeployを構成します。
D. CodeDeployエージェントを設定して、デプロイが失敗したときにAmazon SNSトピックに通知を送信するトリガーを作成します。展開が失敗したときに自動的にロールバックするようにCodeDeployを構成します。
Answer: D

NEW QUESTION: 2
미디어 회사에는 30TB의 디지털 뉴스 비디오 저장소가 있습니다. 이 비디오는 온-프레미스 테이프 라이브러리의 테이프에 저장되며 MAM (Media Asset Management) 시스템에서 참조합니다. 회사는 이러한 비디오의 메타 데이터를 자동화 된 방식으로 보강하고 MAM 기능을 사용하여 검색 가능한 카탈로그에 넣기를 원합니다. 회사는 비디오에서 물체, 풍경 항목 또는 사람의 얼굴과 같은 정보를 기반으로 검색 할 수 있어야합니다. 각 사람의 이미지가 포함 된 비디오에 등장한 사람들의 얼굴이 포함 된 카탈로그를 사용할 수 있습니다. 회사는이 비디오를 AWS로 마이그레이션하려고 합니다.
이 회사는 AWS와 고속 AWS Direct Connect 연결을 가지고 있으며 MAM 솔루션 비디오 컨텐츠를 현재 파일 시스템에서 직접 이동하려고 합니다.
가장 적은 양의 지속적인 관리 오버 헤드를 사용하고 기존 시스템을 최소한으로 중단 시키면 이러한 요구 사항을 어떻게 충족시킬 수 있습니까?
A. 온 프레미스 파일 게이트웨이 어플라이언스 인 AWS Storage Gateway를 설정합니다. MAM 솔루션을 사용하여 현재 아카이브에서 비디오를 추출하여 파일 게이트웨이로 푸시하십시오. 얼굴 카탈로그를 사용하여 Amazon Rekognition에서 컬렉션을 빌드하십시오. Rekognition Javascript SDK를 호출하여 Rekognition이 파일 게이트웨이를 지원하는 Amazon S3 파일에서 비디오를 가져 와서 필요한 메타 데이터를 검색하고 메타 데이터를 MAM 솔루션으로 푸시하도록 AWS Lambda 함수를 구축하십시오.
B. OpenCV 라이브러리를 실행하는 Amazon EC2 인스턴스를 설정합니다. 온 프레미스 라이브러리에서이 EC2 인스턴스에 마운트 된 Amazon EBS 볼륨으로 비디오, 이미지 및 얼굴 카탈로그를 복사하십시오.
비디오를 처리하여 필요한 메타 데이터를 검색하고 비디오 파일을 Amazon S3 버킷으로 복사하는 동안 메타 데이터를 MAM 솔루션으로 푸시하십시오.
C. Amazon Kinesis 비디오 스트림을 사용하여 비디오 수집 스트림을 구성하십시오. 얼굴 카탈로그를 사용하여 Amazon Rekognition에서 컬렉션을 빌드하십시오. MAM 솔루션의 비디오를 Kinesis 비디오 스트림으로 스트리밍합니다. 스트리밍 비디오를 처리하도록 Amazon Rekognition을 구성하십시오. 그런 다음 스트림 소비자를 사용하여 필요한 메타 데이터를 검색하고 메타 데이터를 MAM 솔루션으로 푸시하십시오. Amazon S3에 비디오를 저장하도록 스트림을 구성하십시오.
D. 온 프레미스 테이프 게이트웨이 어플라이언스 인 AWS Storage Gateway를 설정합니다. MAM 솔루션을 사용하여 현재 아카이브에서 비디오를 추출하여 테이프 게이트웨이로 푸시하십시오. 얼굴 카탈로그를 사용하여 Amazon Rekognition에서 컬렉션을 빌드하십시오. Rekognition Javascript SDK를 호출하여 Amazon Rekognition이 테이프 게이트웨이에서 비디오를 처리하고 필요한 메타 데이터를 검색하고 메타 데이터를 MAM 솔루션으로 푸시하도록하는 AWS Lambda 함수를 구축하십시오.
Answer: C
Explanation:
https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html

NEW QUESTION: 3
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
A. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
B. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
C. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
D. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
Answer: A