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NEW QUESTION: 1
A. Option D
B. Option C
C. Option A
D. Option B
Answer: D
NEW QUESTION: 2
通信会社は、顧客の解約を予測する必要があります（つまり、顧客がコンピューターを切り替えることを決定します）。同社には、毎月の消費パターン、顧客サービスへの電話、顧客が最終的にサービスを終了したかどうかなど、各顧客の履歴記録があります。このデータはすべてAmazon S3に保存されます。会社は、忠誠心を取り戻すために、どの顧客がすぐに解約する可能性があるかを知る必要があります。
これらの要件を満たす最適なアプローチは何ですか？
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。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. AWS QuickSightを使用してAmazon S3に保存されたデータに接続し、必要なビジネス洞察を取得します。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
C. EMRを使用してHiveクエリを実行し、顧客のプロファイルを作成します。プロファイルを既存の顧客に適用して、解約の可能性を判断する
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。解約の可能性をコンピューター化するユーザー定義関数をRedshiftで作成する
Answer: A
Explanation:
説明
https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-amazon-machine-learning/
NEW QUESTION: 3
In CA Performance Center, groups are organized in the Groups Tree. Which features characterize the Groups Tree? (Choose two)
A. It includes a sub-node in the Defined Tenants sub-tree for Default Space groups.
B. It is used when an administrator manages groups.
C. It includes the Defined Tenants sub-tree for groups in a tenant space.
D. Its root level is called the Root Group node.
Answer: B,C
