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NEW QUESTION: 1
When RAID runs as part of the operating system on the file server, it is an example of a:
A. network implementation.
B. software implementation.
C. server implementation.
D. hardware implementation.
Answer: B
Explanation:
When RAID runs as part of the operating system on the file server, it is an example of a software implementation. RAID can also be implemented as hardware. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 67.

NEW QUESTION: 2
You are planning a recovery strategy for computers that run Windows 10.
You need to create recovery procedures to roll back feature updates and quality updates.
What should you include in the procedures? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
References:
https://www.thewindowsclub.com/rollback-uninstall-windows-10-creators-update
https://www.dummies.com/computers/pcs/undo-windows-update/

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。
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:
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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question In this section, you will NOT be able to return to it. As a result, these questions will not appear In the review/ screen.
You are the purchasing manager at a manufacturing company that makes audio equipment.
You sign an agreement with a vendor to purchase 5,000 speaker cables. Item C0001, at a discounted rate of S3,00 per cable. This agreement expires in exactly one year.
You need to set up pricing information and track the fulfillment of the agreement.
Solution: On the released product, set a price of $3.00. Add the vendor to the vendor account field on the Purchase fast tab.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/dynamics365/supply-chain/procurement/purchase-agreements

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