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NEW QUESTION: 1
DRAG DROP
Drag and drop EIGRP query condition on the left to the corresponding action taken by the router on the right

Answer:
Explanation:

Explanation: 1:3, 2:1, 3:4, 4:2

NEW QUESTION: 2
Oracle Data Pumpのエクスポートおよびインポート操作には、どの2つの記述が当てはまりますか？ （2つを選択してください）
A. データ・ポンプ・エクスポート・ジョブから切り離し、後で再接続することができます。
B. データ・ポンプのインポートには、インポート・ファイルがOracle所有者の所有するディレクトリに存在する必要があります。
C. Data Pumpは、パラレル実行サーバー・プロセスを使用してパラレル・インポートを実装します。
D. Data Pumpは、すべてのタイプのデータのロードおよびアンロードに常にダイレクト・パス方式を自動的に使用します。
E. データポンプのインポートジョブから切り離し、後で再接続することができます。
Answer: A,C

NEW QUESTION: 3
Which two statements are true regarding HDP pools? (Choose two.)
A. HDP pools can be used for sizing HDT pools.
B. Using the largest possible pools is a recommended practice.
C. Exposure to failure and recovery from failure must be considered when recommending the maximum size of a pool.
D. Tier migration is based on performance and space capacity.
Answer: A,B

NEW QUESTION: 4
グローバルな靴屋には、自分で管理したり、他のディスカウントストアでブローカーを通じて販売したり、オンラインで販売したりする1000を超える小売店があります。これらのチャンネルは完全に独立しています。 COOが包括的な洞察を得ることを可能にするためにこれらのデータセットをマージするシステムはありません。あなたは、そのチャネルの全社的な全体像を提供し、必要に応じてアドホック分析を実行できるようにする必要があります。これをどのように設定できますか？ 2つ選んでください。
2正しい答えを選んでください。
A. Amazon EMRを使用して、分析クエリが効率的かつ並列に実行できるように、データをテーブルにロード、ソート、分散、および圧縮します。最後に、分析を視覚化するために、Amazon QuickSight、またはAmazon EMRへのOBDC / JDBC接続を介した多数のパートナー視覚化プラットフォームの1つを使用できます。
B. まず、さまざまなソースからすべてのデータをAmazon DynamoDBに取得します。次に、Amazon Redshiftを使用して、データを変換元の形式から変換先および形式に変換します。
C. まず、さまざまなソースからすべてのデータをAmazon S3に渡します。次に、Amazon EMRを使用して、データをソース形式から宛先形式および目的の形式に変換して消去します。
D. Amazon Redshiftを使用してデータをそのテーブルにロード、ソート、分散、および圧縮して、分析クエリが効率的かつ並行して実行できるようにします。最後に、分析を視覚化するために、Amazon QuickSight、またはAmazon RedshiftへのOBDC / JDBC接続を介した多数のパートナー視覚化プラットフォームの1つを使用できます。
Answer: C,D
Explanation:
An enterprise data warehouse is a terrific way to solve this problem. The data warehouse needs to collect data from each of the three channels'various systems and from public records for weather and economic data. Each data source sends data daily for consumption by the data warehouse. Because each data source may be structured differently, an extract, transform, and load (ETL) process is performed to reformat the data into a common structure. Then, analytics can be performed across data from all sources simultaneously Reference:
https://d0.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf