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NEW QUESTION: 1
An architect is helping an organization with the Logical Design of an NSX-T Data Center solution. This information was gathered during the Assessment Phase:
* Maximum performance and availability Is required between the physical and virtual network.
* Load Balancing service is required for back-end web servers.
* NAT is required.
Which should the architect include in their design?
A. Deploy an Active/Active Tier-0 gateway and configure ECMP.
B. Deploy an Active/Passive Tier-0 gateway and configure ECMP.
C. Deploy a Tler-1 gateway and connect It to an Active/Active Tier-0 gateway with ECMP configured.
D. Create two separate VLANs to connect the Tier-0 gateway upstream traffic and configure ECMP.
Answer: B

NEW QUESTION: 2
Which of the following statements are correct regarding copying and follow up if a transaction?
Please choose the correct answer.
Response:
A. Transaction history is updated after copying a transaction.
B. Header data is copied and you can select items during copying a transaction.
C. You can choose the transaction type during follow up.
D. Whether a transaction type can be copied or not, is customized in the copying control of transactions.
Answer: C

NEW QUESTION: 3
ケーススタディ：6 - TerramEarth
会社概要
TerramEarthは、鉱業および農業業界向けの重機を製造しています。約
彼らの事業の80％は鉱業から、そして20％は農業からのものです。現在、100カ国に500以上のディーラーとサービスセンターがあります。彼らの使命は、顧客をより生産的にする製品を作ることです。
ソリューションコンセプト
1秒間に120フィールドのデータを収集する2,000万台のTerramEarth車両が稼働しています。
データは車両にローカルに保存され、車両の修理時に分析のためにアクセスできます。
データはメンテナンスポートを介してダウンロードされます。この同じポートを使用して動作パラメータを調整し、新しいコンピューティングモジュールで現場での車両のアップグレードを可能にすることができます。
約200,000台の車両が携帯電話ネットワークに接続されているため、TerramEarthはデータを直接収集できます。 1日あたり22時間の運用で1秒あたり120フィールドのデータレートで、TerramEarthはこれらの接続された車両から合計約9 TB /日を収集します。
既存の技術環境
TerramEarthの既存のアーキテクチャは、単一の米国西海岸ベースのデータセンターに常駐するLinuxおよびWindowsベースのシステムで構成されています。これらのシステムは現場からCSVファイルをgzipしてFTPでアップロードし、データをデータウェアハウスに保存します。このプロセスには時間がかかるため、集約レポートは3週間前のデータに基づいています。
このデータを使用して、TerramEarthは交換部品の優先在庫を確保し、計画外のダウンタイムを60％削減することができました。しかし、データが古くなっているため、一部の顧客は交換部品を待つ間、最長4週間車を購入できません。
ビジネス要件
計画外の車両のダウンタイムを1週間以内に減らします。
* Support the dealer network with more data on how their customers use their equipment to better
* position new products and services
Have the ability to partner with different companies - especially with seed and fertilizer suppliers
* in the fast-growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east
* coast.
Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
* Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
* - 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
* - Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
* - RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
エグゼクティブステートメント
当社の競争上の優位性は常に製造プロセスにあり、競合他社よりも低コストでより良い車両を製造することができます。しかし、さまざまなアプローチを持つ新製品が絶えず開発されており、私たちの業界で次の変革の波を経験するためのスキルが不足していることを私は心配しています。私の目標は、段階的な技術革新を通じて、当面の市場のニーズに対応しながら、私たちのスキルを磨くことです。
この質問については、TerramEarthのケーススタディを参照してください。技術的要件を考慮して、GCPの計画外の車両停止時間をどのように減らすべきですか？
A. データウェアハウスとしてBigQueryを使用してください。すべての車両をネットワークに接続し、Cloud Pub / SubとCloud Dataflowを使用してBigQueryにデータをストリーミングします。分析とレポート作成にはGoogle Data Studioを使用してください。
B. データウェアハウスとしてCloud Dataproc Hiveを使用します。直接Hrtテーブルにデータをストリーミングします。
Pigスクリプトを使用してデータを分析してください。
C. データウェアハウスとしてBigQueryを使用します。すべての車両をネットワークに接続し、gcloudを使用してgzipファイルをMulti-Regional Cloud Storageバケットにアップロードします。分析とレポート作成にはGoogle Data Studioを使用してください。
D. データウェアハウスとしてCloud Dataproc Hiveを使用します。 gzipファイルをMultiRegional Cloud Storageバケットにアップロードします。 gcloudを使用してこのデータをBigQueryにアップロードします。分析とレポート作成にはGoogle data Studioを使用してください。
Answer: A