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NEW QUESTION: 1
Your network contains an Active Directory domain named contoso.com. The domain contains a server named Server1. Server1 runs Windows Server 2012 R2. You need to create 3-TB virtual hard disk (VHD) on Server1.
Which tool should you use?
A. File Server Resource Manager (FSRM)
B. New-StoragePool
C. Diskpart
D. Share and Storage Management
Answer: C
Explanation:
Explanation/Reference:
Explanation:
You can create a VHD from either the Disk Management snap-in or the command line (diskpart).
From the DiskPart command-line tool at an elevated command prompt, run the create vdisk command and specify the file (to name the file) and maximum (to set the maximum size in megabytes) parameters. The following code demonstrates how to create a VHD file at C:\vdisks\disk1.vdh with a maximum file size of 16 GB (or 16,000 MB).
DiskPart
Microsoft DiskPart version 6.1.7100
Copyright (C) 1999-2008 Microsoft Corporation.
On computer: WIN7
DISKPART> create vdisk file="C:\vdisks\disk1.vhd" maximum=16000

NEW QUESTION: 2
Which of the following represents the best method of ensuring business unit alignment with security program requirements?
A. Provide clear communication of security requirements throughout the organization
B. Perform increased audits of security processes and procedures
C. Create collaborative risk management approaches within the organization
D. Demonstrate executive support with written mandates for security policy adherence
Answer: C

NEW QUESTION: 3
Microsoft AzureでLambdaアーキテクチャを使用してソリューションを開発しています。
テストレイヤーのデータは、次の要件を満たしている必要があります。
データストレージ：
*リポジトリ（またはさまざまな形式の大量の大容量ファイル）として機能します。
*ビッグデータ分析ワークロード用に最適化されたストレージを実装します。
*階層構造を使用してデータを整理できるようにします。
バッチ処理：
*メモリ内の計算処理にはマネージドソリューションを使用します。
* Scala、Python、およびRプログラミング言語をネイティブにサポートします。
*クラスターを自動的にサイズ変更および終了する機能を提供します。
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Lambdaアーキテクチャを構築するには、正しいテクノロジーを特定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
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is