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NEW QUESTION: 1
In an EMC NetWorker backup environment, where do the metadata and backup policies reside?
A. Storage node
B. Dedicated storage node
C. Backup server
D. Backup client
Answer: B

NEW QUESTION: 2
You have a database named Sales.
You need to create a table named Customer that includes the columns described in the following table:

How should you complete the Transact SQL statement? To answer, select the appropriate Transact-SQL segments in the answer area.

Answer:
Explanation:

Explanation

Box 1: MASKED WITH (FUNCTION ='default()')
The Defualt masking method provides full masking according to the data types of the designated fields.
Example column definition syntax: Phone# varchar(12) MASKED WITH (FUNCTION = 'default()') NULL Box 2: MASKED WITH (FUNCTION ='partial(3,"XXXXXX",0)') The Custom String Masking method exposes the first and last letters and adds a custom padding string in the middle. prefix,[padding],suffix examples:
PhoneNumber varchar(10) MASKED WITH (FUNCTION = 'partial(5,"XXXXXXX",0)') Box 3: MASKED WITH (FUNCTION ='email()') The Email masking method which exposes the first letter of an email address and the constant suffix ".com", in the form of an email address. [email protected].
Example definition syntax: Email varchar(100) MASKEDWITH (FUNCTION = 'email()') NULL References: https://msdn.microsoft.com/en-us/library/mt130841.aspx

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、精度、精度、リコール、F1スコア、およびAUC。
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: A
Explanation:
Accuracy, Precision, Recall, F1 score, and AUC are metrics for evaluating classification models.
Note: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error are OK for the linear regression model.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model