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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Azure Learning Learning Studioで新しい実験を作成しています。
1つのクラスには、トレーニングの他のクラスよりもはるかに少ない数の観測値があります。クラスの不均衡を補うために、適切なデータサンプリング戦略を選択する必要があります。
解決策：Synthetic Minority Oversampling Technique（SMOTE）サンプリングモードを使用します。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: B
Explanation:
SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 2
What are three factors a network administrator must consider before implementing Netflow in the network? (Choose three.)
A. CPU utilization
B. SNMP version
C. port availability
D. number of devices exporting Netflow data
E. where Netflow data will be sent
F. WAN encapsulation
Answer: A,D,E

NEW QUESTION: 3
What is the output of the following code?

A. false-true
B. true-true
C. false-false
D. true-false
Answer: A