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NEW QUESTION: 1
A network design team is experiencing sustained congestion on access and distribution uplinks. QoS has already been implemented and optimized, and it is no longer effective in ensuring optimal network performance. Which two actions can improve network performance? (Choose two.)
A. Configure selective packet discard to drop noncritical network traffic.
B. Implement higher-speed uplink interfaces.
C. Bundle additional uplinks into logical EtherChannels.
D. Reconfigure QoS based on the IntServ model.
E. Utilize random early detection to manage queues.
Answer: B,C

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
DRAG DROP
You are developing a web application that integrates with Azure Active Directory (AD). The application uses the OAuth 2.0 protocol to authorize secure connections to a web service that is at https://service.adatum.com.
The application must request an access token to invoke the web service methods.
You need to submit an HTTP request to the Azure AD endpoint.
How should you complete the request? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.

Answer:
Explanation: