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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、指定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がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、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。
User2という名前のユーザーには、Windows 10を実行するComputer2という名前のコンピューターがあります。User2は、user2 @ contoso.comを使用してComputer2をcontoso.comに参加させます。
Computer1には、Folder1という名前のフォルダーが含まれています。 Folder1はドライブCにあり、Share1として共有されています。 Share1には、次の表に示す権限があります。

User2という名前のユーザーには、Windows 10を実行するComputer2という名前のコンピューターがあります。User2は、user2 @ contoso.comを使用してComputer2をcontoso.comに参加させます。
User2はShare1へのアクセスを試み、次のエラーメッセージを受け取ります。「ユーザー名またはパスワードが正しくありません。」 User2がShare1に接続できることを確認する必要があります。
解決策：Computer1にローカルグループを作成し、Guestアカウントをグループに追加します。 Share1へのグループ変更アクセス権を付与します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B

NEW QUESTION: 2
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。
次の変数に応じて、学生のアートワークの価格を予測するモデルを作成しています：学生の教育期間、学位タイプ、および芸術形態。
まず、線形回帰モデルを作成します。
線形回帰モデルを評価する必要があります。
解決策：次のメトリックを使用します：平均絶対誤差、ルート平均絶対誤差、相対絶対誤差、相対二乗誤差、および決定係数。
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:
Explanation
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:
ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 3
HOTSPOT
Your network contains an Active Directory domain named contoso.com. The domain contains a server named Server1 that runs Windows Server 2016.
You install IP Address Management (IPAM) on Server1. You select the automatic provisioning method, and then you specify a prefix of IPAM1.
You need to configure the environment for automatic IPAM provisioning.
Which cmdlet should you run? To answer, select the appropriate options in the answer area.

Answer:
Explanation:
Invoke-IpamGpoProvisioning -Domain contoso.com -GpoPrefixName IPAM -IpamServerFqdn dc1.contoso.com

NEW QUESTION: 4
What command will remove a physical volume from a volume group? (Specify ONLY the command without any path or parameters.)
Answer:
Explanation:
vgreduce,
/sbin/vgreduce