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NEW QUESTION: 1
Which of the following is a type of countermeasure that can be deployed to ensure that a threat vector does not meet a vulnerability?
A. Detection controls
B. Monitoring controls
C. Prevention controls
D. Subversive controls
Answer: C

NEW QUESTION: 2
システム管理者は、テレセールスチームがオフィスの外でSalesforceにログインできないようにする必要があります。
彼/彼女はこれをどのように行いますか？
A. この機能を有効にする必要があるため、salesforce.comにお問い合わせください
B. チームのプロファイルにIPアドレスの範囲を追加します
C. これを行う方法はありません
D. セットアップ|セキュリティ管理|ネットワークアクセスとチームのIPアドレスの範囲の指定
Answer: B

NEW QUESTION: 3
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