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NEW QUESTION: 1
Which method should you choose to manage setup data if you have a need to configure and modify the default setup best practices? (Choose the best answer.)
A. Rapid Implementation Task List
B. Manage Offerings and Features
C. Manage Implementation Projects
D. Configure Basic Enterprise Structure
Answer: C

NEW QUESTION: 2
付属書類を参照してください、 コンピュータAがコンピュータにpingできないと判断した。問題の原因はどれですか？

you determine that Computer A cannot ping Computer. Which reason for the problem is most likely true?
A. コンピュータAのデフォルトゲートウェイアドレスが正しくありません。
B. コンピュータBのデフォルトゲートウェイアドレスが正しくありません。
C. コンピュータAのサブネットマスクが正しくない。
D. コンピュータBのサブネットマスクが正しくありません。
Answer: C
Explanation:
255.255.255.224 =/27

NEW QUESTION: 3
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。
複数の列に欠損値を含む数値データセットを分析しています。
機能セットの次元に影響を与えることなく、適切な操作を使用して欠損値を消去する必要があります。
すべての値を含めるには、完全なデータセットを分析する必要があります。
解決策：連鎖方程式による多重代入（MICE）メソッドを使用して、各欠損値を置き換えます。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: A
Explanation:
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data