About NetSec-Generalist J' userlist.txt newlist.txt
B. tr -d '\ r' <userlist.txt> newlist.txt
C. tr '\ r' '\ n' userlist.txt newlist.txt
D. tr '\ r \ n' '' <userlist.txt> newlist.txt
E. tr -c '\ n \ r' '' <newlist.txt> userlist.txt
Answer: B
NEW QUESTION: 4
The ICMP monitor has been assigned to all nodes. In addition, all pools have been assigned custom monitors. If a pool .eb.is marked .vailable (green) which situation is sufficient to cause this.
A. All of the .eb.pool. member. nodes are responding to the ICMP monitor as expected.
B. All of the members of the .eb.pool have had their content updated recently and their responses no longer match the monitor. receive rule.
C. Less than 50% of the .eb.pool. member. nodes responded to the ICMP echo request.
D. Over 25% of the .eb.pool. members have had their content updated and it no longer matches the receive rule of the custom monitor.
The other respond as expected.The other. respond as expected.
Answer: D
Exam Questions
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NEW QUESTION: 1
During node protection signaling, the PLR knows the address of the MP from which of the following methods?
A. The PLR learns the address of the MP from the RRO
B. The PLR does not need to know the address of the MP.
C. The PLR learns the address of the MP from the ERO.
D. The PLR learns the address of the MP from the targeted RSVP hello message.
E. The PLR issues a RSVP path message to the destination router to request the address of the MP.
Answer: A
Explanation:
Section: Volume A
NEW QUESTION: 2
You create a binary classification model.
You need to evaluate the model performance.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. relative absolute error
B. precision
C. accuracy
D. mean absolute error
E. coefficient of determination
Answer: B,C
Explanation:
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: 'Out of the individuals whom the model, how many were classified correctly (TP)?' This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
NEW QUESTION: 3

A. tr -s '