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NEW QUESTION: 1
You have distribution group named Car Parking Distribution Group that is configured as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

Answer:
Explanation:

Explanation

References:
https://docs.microsoft.com/en-us/powershell/module/exchange/users-and-groups/set-distributiongroup?view=exc

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. はい
B. いいえ
Answer: A
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
universal container provides installation services for each of its products. Each product requires different number of times, and a different sets of installation tasks How should a consultant configure field service lightning to support this service
A. Use the work type to define the installation services for each product- use work order to track work for each customer - use service appointment to assign the work
B. Use service appointment to define the installation services for each product - use work order to track order for each customer use task to assign the work
C. Use cases to define the installation services for each product - use service appointment to track the work for each customer- use work order to assign the work
D. Use work order to define the installation services for each product use work type to each work - use work type to track work for each customer - use service appointment to assign the work
Answer: A

NEW QUESTION: 4

NAME TYPE VALUE
------------------------------------------------
optimizer_adaptive_reporting_only boolean FALSE
optimizer_capture_sql_plan_baselines boolean FALSE
optimizer_dynamic_sampling integer 2
optimizer_features_enable string 12.1.0.1

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