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NEW QUESTION: 1
コンピューティングインスタンスに複数のアプリケーションがインストールされており、これらのアプリケーションが大量のログファイルを生成します。これらのログファイルは、少なくとも15日間はブートボリュームに常駐し、少なくとも60日間は保持される必要があります。 60日間の保持要件により、使用可能なディスク領域に問題が発生しています。
このコンピューティングインスタンスに追加のブートボリュームスペースを提供するために推奨される2つの方法は何ですか？
（2つ選択してください。）
A. ブロックボリュームを作成してコンピューティングインスタンスに接続し、ログファイルをコピーします。
B. カスタムスクリプトを作成してログファイルを毎日削除し、ブートボリュームのスペースを解放します。
C. カスタムイメージを作成し、より大きなブートボリュームサイズで新しいコンピューティングインスタンスを起動します。
D. オブジェクトストレージバケットを作成し、毎日実行するスクリプトを使用して、15日より古いログファイルをバケットに移動します。
E. ブートボリュームを保持しながらインスタンスを終了します。ブートボリュームから新しいインスタンスを作成し、DenseIOシェイプを選択して、ローカルNVMeストレージを利用します。
Answer: A,D

NEW QUESTION: 2
Which three Cisco WLC v7.0 CLI family of commands would be appropriate to troubleshoot a wireless client failure for connection to an AP? (Choose three.)
A. debug capwap
B. debug aaa
C. debug mac addr
D. debug dhcp
E. debug ap
F. debug dtls
G. debug ccxdiag
Answer: B,C,D

NEW QUESTION: 3

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

NEW QUESTION: 4
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Permutation Feature Importance
C. Filter Based Feature Selection
D. Synthetic Minority Oversampling Technique (SMOTE)
Answer: D
Explanation:
Explanation
Use the SMOTE module in Azure Machine Learning Studio (classic) 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.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote