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NEW QUESTION: 1
Which of the following does NOT define the Eclipse platform?
A. Eclipse is an IDE framework which only runs on Linux operating system.
B. Eclipse is extensible through plug-in architecture.
C. Eclipse is a kind of universal tool platform-an open extensible IDE for anything and nothing in particular.
D. Eclipse is an IDE framework donated to the Open Source community by IBM.
Answer: A

NEW QUESTION: 2
EBS 볼륨에서 데이터를 어떻게 안전하게 보호 할 수 있습니까?
A. S3 서버 측 암호화 서비스를 사용하여 볼륨을 암호화합니다.
B. EC2의 SSL 인터페이스를 사용하여 인스턴스에 볼륨을 연결하십시오.
C. EBS 볼륨 위에 암호화된 파일 시스템을 사용하십시오.
D. 볼륨에 대한 읽기 및 쓰기 액세스를 제한하는 IAM 정책을 만듭니다.
E. 순차적으로 쓰는 대신 임의로 데이터를 씁니다.
Answer: C

NEW QUESTION: 3
サードパーティから毎月CSV形式のデータファイルを受け取ります。このデータをクレンジングする必要がありますが、3か月ごとにファイルのスキーマが変更されます。これらの変換を実装するための要件は次のとおりです。
*スケジュールに従って変換を実行する
*開発者以外のアナリストが変換を変更できるようにする
*変換を設計するためのグラフィカルツールを提供する
あなたは何をするべきか？
A. Cloud Dataprepを使用して変換レシピを構築および維持し、スケジュールに基づいて実行します
B. アナリストがPythonでCloudDataflowパイプラインを作成して変換を実行できるようにします。 Pythonコードはリビジョン管理システムに保存し、受信データのスキーマが変更されたときに変更する必要があります
C. 毎月のCSVデータをBigQueryに読み込み、SQLクエリを記述してデータを標準スキーマに変換します。変換されたテーブルをSQLクエリとマージします
D. Apache Spark on Cloud Dataprocを使用して、データフレームを作成する前にCSVファイルのスキーマを推測します。
次に、データをCloud Storageに書き出してBigQueryに読み込む前に、SparkSQLで変換を実装します
Answer: D

NEW QUESTION: 4
次の条件を満たすように、クラウドネイティブの履歴データ処理システムを設計しています。
*分析対象のデータはCSV、Avro、PDF形式であり、Cloud Dataproc、BigQuery、ComputeEngineなどの複数の分析ツールからアクセスできます。
*ストリーミングデータパイプラインは、新しいデータを毎日保存します。
*パフォーマンスはソリューションの要素ではありません。
*ソリューションの設計では、可用性を最大化する必要があります。
このソリューションのデータストレージをどのように設計する必要がありますか？
A. 地域のCloudStorageバケットにデータを保存します。 Cloud Dataproc、BigQuery、ComputeEngineを使用してバケットに直接アクセスします。
B. データをマルチリージョンのCloudStorageバケットに保存します。 Cloud Dataproc、BigQuery、ComputeEngineを使用してデータに直接アクセスします。
C. 高可用性を備えたCloudDataprocクラスターを作成します。データをHDFSに保存し、必要に応じて分析を実行します。
D. データをBigQueryに保存します。 CloudDataprocとComputeEngineのBigQueryコネクタを使用してデータにアクセスします。
Answer: A