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NEW QUESTION: 1
Drag the task on the left to the appropriate layer category on the right.

Answer:
Explanation:

Explanation

NEW QUESTION: 2
How are analytics capabilities built into SAP S/4HANA Cloud?
A. They are installed on a dedicated SAP Fiori frontend server
B. They are installed as an add-on to the application
C. They are extended by the user to a dedicated SAP Fiori frontend server
D. They are embedded in the same technical stack as the application
Answer: D

NEW QUESTION: 3
Which annotation enables a servlet to efficiently process requests of typo multipart/form-data that involve large files being uploaded by the client?
A. @AcceptMultipart
B. @WebServlet (multipart = true)
C. @MultiPartFormData
D. @MultiPartConfig
Answer: D
Explanation:
Explanation/Reference:
Reference: http://www.scribd.com/ilinchen2008/d/38764279-Servlet3-0-Specs (page 22, last paragraph)

NEW QUESTION: 4
2クラスのロジスティック回帰モデルを使用して、バイナリ分類を作成しています。
不均衡のモデル結果を評価する必要があります。
どの評価基準を使用する必要がありますか？
A. 相対絶対誤差
B. AUCカーブ
C. 相対二乗誤差
D. 平均絶対誤差
Answer: B
Explanation:
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model