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NEW QUESTION: 1
Ein IS-Prüfer kommt zu dem Schluss, dass die Zugriffssicherheit eines lokalen Netzwerks (LAN) zufriedenstellend ist. Bei der Überprüfung der Arbeit sollte der Prüfungsleiter
A. Beurteilen Sie, ob der Prüfer über die erforderlichen Fähigkeiten verfügt, um die Arbeit auszuführen
B. Führen Sie einige Schritte des Audits erneut durch, um die Qualität der Arbeit zu überprüfen
C. Stellen Sie sicher, dass die Elemente eines vereinbarten Prüfungsplans berücksichtigt wurden
D. Überprüfen Sie die Übereinstimmung der Benutzerverwaltung mit den Ergebnissen
Answer: C

NEW QUESTION: 2
Was ist ein Zweck eines Unterrichtsprotokolls?
A. Um die Lektionen an die Projektplatine weiterzuleiten
B. Zum Aufzeichnen von Lektionen, die in Checkpoint-Berichten enthalten sind
C. Verbesserungen mit Projektsicherheit erhöhen
D. Um zu dokumentieren, dass Korrekturmaßnahmen ergriffen werden
Answer: B
Explanation:
Explanation
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NEW QUESTION: 3
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance