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NEW QUESTION: 1
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation
Create Scatterplot
Summarize Data
Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

NEW QUESTION: 2
In the Security Policy shown here, which rule inhibits Rule 4?

A. Rule 2
B. No rule inhibits Rule 4.
C. Rule 3
D. Rule 1
Answer: B
Explanation:
Rule 3 would have no affect on rule 4. Rule 3 is the stealth rule and is blocking any traffic to the firewall which would have no affect on rule 4. The biggest issue is that with any of the three types of authentication, the source of an authentication rule must be a group. The real reason this policy would not install is because the source of rule 4 does not include a group.

NEW QUESTION: 3
展示に言及します。

どのように、不適当にセットされたテルネット/SSHパラメータが、シスコWCSによるコントローラの構成をもたらします？
A. シスコWCSがsessionを開ける前にこれらの証明書をチェックするので、CLIとコントローラのGUI管理は両方とも失敗します
B. シスコWCSがcontrollerにログインにテルネット/SSHパラメータを使うので、コントローラのGUI管理は失敗します
C. テルネット/SSHユーザー名には、CLI templatesでコマンドを実行するために、十分な特権がなければなりません
D. コントローラはシスコWCSで構成されるままです、しかし、管理は他のクライアントbrowsersからGUIによって可能でありません
Answer: C

NEW QUESTION: 4
AccessibilityToHighway列の欠落データを置き換える必要があります。
Clean Missing Dataモジュールをどのように構成する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

説明

ボックス1：MICEを使用して交換
MICEを使用して置換：欠損値ごとに、このオプションは新しい値を割り当てます。これは、統計文献で「連鎖方程式を使用した多変量代入」として説明されている方法を使用して計算されます。
「連鎖方程式による多重代入」。多重代入法では、欠損値を埋める前に、データ内の他の変数を使用して、欠損データを持つ各変数が条件付きでモデル化されます。
シナリオ：両方のデータセットのAccessibilityToHighway列に欠損値が含まれています。欠損値を埋める前に、データ内の他の変数を使用して条件付きでモデル化されるように、欠損データを新しいデータに置き換える必要があります。
ボックス2：伝播
すべての欠損値を含む列は、すべての欠損値の列を出力で保持する必要があるかどうかを示します。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data