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NEW QUESTION: 1
特徴抽出方法を選択する必要があります。
どの方法を使用する必要がありますか？
A. ケンドール相関
B. 相互情報
C. ムードの中央値検定
D. 順列機能の重要性
Answer: A
Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules
NEW QUESTION: 2
侵入検知システム（IDS）の最近の実装を評価する場合、情報システム監査人は最も不適切であることに注意する必要があります。
A. チューニング
B. 暗号化
C. トレーニング
D. パッチ
Answer: A
NEW QUESTION: 3
ISEをポスチャ用に設定するときに有効な2つの条件はどれですか？ （2つ選択してください。）
A. Profile status
B. Dictionary
C. File
D. memberOf
E. Service
Answer: C,E