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NEW QUESTION: 1
A Citrix Administrator turned on Provisioning Services auditing.
Changes made in the __________, __________ and __________ will be recorded. (Choose the three correct locations to complete the sentence.)
A. SOAP Server
B. MCLI
C. Provisioning Services Console
D. Provisioning Services Boot Device Manager
E. Provisioning Services BOOTPTAB Editor
Answer: A,B,C

NEW QUESTION: 2
A customer has an EMC Data Domain integrated into their Symantec NetBackup domain as an OST device at their production site. They are installing a new NetBackup media server and a second Data Domain at their disaster recovery (DR) site to protect their client backup images.
How should replication be configured to allow NetBackup to recover client backup data from the DR site in the event the Data Domain at the production site goes offline?
A. Use fastcopy at the production site to make a copy of the NetBackup images and replicate them to the DR site
B. Configure NetBackup to duplicate the images between the Data Domain OST devices
C. Configure MTree replication from the production site to the DR site
D. Perform snapshots of the NetBackup image directories and replicate those snapshots to the DR site
Answer: B

NEW QUESTION: 3
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. 相対二乗誤差
B. 平均絶対誤差
C. 相対絶対誤差
D. AUCカーブ
Answer: D
Explanation:
説明
受信者動作特性（ROC）曲線および対応する曲線下面積（AUC）値で、真陽性率と偽陽性率を調べることができます。この曲線が左上隅に近いほど、分類器のパフォーマンスは向上します（つまり、偽陽性率を最小化しながら真陽性率を最大化します）。プロットの対角線に近い曲線は、ランダムな推測に近い予測を行う傾向がある分類器の結果です。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binar