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NEW QUESTION: 1
会社は、Platform-as-a-Service（PaaS）を使用して新しいデータパイプラインプロセスを作成する予定です。プロセスは次の要件を満たしている必要があります。
摂取：
*複数のデータソースにアクセスする
*ワークフローを調整する機能を提供する
* SQL Server Integration Servicesパッケージを実行する機能を提供します。
格納：
*ビッグデータワークロード向けにストレージを最適化します。
*保存データの暗号化を提供します。
*サイズ制限なしで動作します。
準備とトレーニング：
*探索と視覚化のための完全に管理されたインタラクティブなワークスペースを提供します。
* R、SQL、Python、Scala、およびJavaでプログラミングする機能を提供します。
* Azure Active Directoryでシームレスなユーザー認証を提供します。
モデルとサービス：
*ネイティブのカラムナーストレージを実装します。
* SQL言語のサポート
*構造化ストリーミングのサポートを提供します。
データ統合パイプラインを構築する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。

Answer:
Explanation:

Explanation

Ingest: Azure Data Factory
Azure Data Factory pipelines can execute SSIS packages.
In Azure, the following services and tools will meet the core requirements for pipeline orchestration, control flow, and data movement: Azure Data Factory, Oozie on HDInsight, and SQL Server Integration Services (SSIS).
Store: Data Lake Storage
Data Lake Storage Gen1 provides unlimited storage.
Note: Data at rest includes information that resides in persistent storage on physical media, in any digital format. Microsoft Azure offers a variety of data storage solutions to meet different needs, including file, disk, blob, and table storage. Microsoft also provides encryption to protect Azure SQL Database, Azure Cosmos DB, and Azure Data Lake.
Prepare and Train: Azure Databricks
Azure Databricks provides enterprise-grade Azure security, including Azure Active Directory integration.
With Azure Databricks, you can set up your Apache Spark environment in minutes, autoscale and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch and scikit-learn.
Model and Serve: SQL Data Warehouse
SQL Data Warehouse stores data into relational tables with columnar storage.
Azure SQL Data Warehouse connector now offers efficient and scalable structured streaming write support for SQL Data Warehouse. Access SQL Data Warehouse from Azure Databricks using the SQL Data Warehouse connector.
References:
https://docs.microsoft.com/bs-latn-ba/azure/architecture/data-guide/technology-choices/pipeline-orchestration-da
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks

NEW QUESTION: 2
品質保証イニシアチブの一環として、組織は外部の監査人を雇って内部のIS監査機能を評価しました。次の観察のうち最も懸念すべきものはどれですか？
A. 監査レポートは監査委員会によって承認されていません。
B. 監査チームはデータ分析を十分に活用していません。
C. 監査契約はリスクベースではありません。
D. 監査レポートには、業界基準に従って実施されたことが記載されていません。
Answer: C

NEW QUESTION: 3
EMRFS를 사용하는 Amazon EMR 클러스터는 Amazon S3에서 페타 바이트 단위의 데이터에 액세스 할 수 있으며 여러 고유 한 데이터 소스에서 생성됩니다. 고객은 대화식 조인을 수행하고 결과를 신속하게 표시 할 수 있도록 일부 데이터 세트에서 공통 필드를 조회해야 합니다.
어떤 기술이 이 기능을 사용하기에 가장 적합한가?
A. 돼지
B. R Studio
C. MicroStrategy
D. Presto
Answer: A