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NEW QUESTION: 1
Following is the list of locations in random order where oranfstab can be placed.
1./etc/mtab
2.$ORACLE_HOME/dbs/oranfstab
3./etc/oranfstab
What is the sequence in which Direct NFS will search the locations?
A. 1, 2, 3
B. 2, 3, 1
C. 3, 2, 1
D. 1, 3, 2
Answer: B
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
Which of the following laptop display types relies on a backlight for the display presentation?
A. CRT
B. LED
C. LCD
D. Plasma
Answer: C
Explanation:
http://electronics.howstuffworks.com/lcd.htm

NEW QUESTION: 3
Welche der folgenden Aussagen bezieht sich auf ein Authentifizierungsprotokoll, für das zwei unabhängige Methoden zum Festlegen von Identität und Berechtigungen erforderlich sind?
A. Zwei-Faktor-Authentifizierung
B. Authentifizierung mit zwei Passwörtern
C. Rich-Factor-Authentifizierung
D. Authentifizierung mit zwei Kennwörtern
E. Authentifizierung mit starkem Faktor
F. Authentifizierung mit zwei Schlüsseln
Answer: A
Explanation:
Erläuterung:
Die Zwei-Faktor-Authentifizierung (T-FA) bezieht sich auf jedes Authentifizierungsprotokoll, für das zwei unabhängige Methoden zum Herstellen von Identität und Berechtigungen erforderlich sind. Gängige Implementierungen der Zwei-Faktor-Authentifizierung verwenden "etwas, das Sie kennen" als einen der beiden Faktoren und verwenden entweder "etwas, das Sie haben" oder "etwas, das Sie sind" als den anderen Faktor. Die Verwendung von mehr als einem Faktor wird auch als starke Authentifizierung bezeichnet. Auf der anderen Seite wird die Verwendung nur eines Faktors von einer schwachen Authentifizierung berücksichtigt.

NEW QUESTION: 4
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
A. Use the class distribution to generate 10% positive examples
B. Use a convolutional neural network with max pooling and softmax activation
C. Downsample the data with upweighting to create a sample with 10% positive examples
D. Remove negative examples until the numbers of positive and negative examples are equal
Answer: D