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NEW QUESTION: 1
電気通信会社は、顧客の混乱（すなわち、競合他社に切り替えることを決定した顧客）を予測する必要がある。同社は、毎月の消費パターン、顧客サービスへの電話、および顧客が最終的にサービスを終了したかどうかなど、各顧客の履歴記録を持っています。このデータはすべてAmazon S3に保存されています。会社は、どの顧客がすぐに解約する可能性があるかを知っている必要があります。
これらの要件を満たすための最適な方法は何ですか？
A. Amazon Machine Learningサービスを使用して、Amazon S3に保存されているデータセットに基づいてバイナリ分類モデルを構築します。このモデルは、既存の顧客の解約属性を予測するために定期的に使用されます。
B. EMRを使用してHiveクエリを実行し、激動する顧客のプロファイルを作成します。解約の可能性を判断するために、既存の顧客にプロファイルを適用します。
C. AWS QuickSightを使用してそれをAmazon S3に格納されているデータに接続し、必要なビジネス上の洞察を得ます。解約傾向グラフをプロットして、既存の顧客の解約可能性を推定します。
D. Redshiftクラスターを使用してAmazon S3からデータをコピーします。解約の可能性を計算するRedshiftでユーザー定義関数を作成します。
Answer: C

NEW QUESTION: 2
DRAG DROP
Drag the hash or algorithm from the left column to its appropriate category on the right.

Answer:
Explanation:

https://www.cisco.com/c/en/us/about/security-center/next-generation-cryptography.html

NEW QUESTION: 3

A. Option D
B. Option B
C. Option C
D. Option A
Answer: A