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

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