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NEW QUESTION: 1
Which three fields can be included in a pcap filter? (Choose three.)
A. Rule number
B. Destination IP
C. Egress Interface
D. Source IP
E. Ingress Interface
Answer: A,B,D
Explanation:
Explanation/Reference:
BD: Following are few filter examples (though NOT limited solely to these options) which can be referenced/utilized/applied:
Filter By Port
> tcpdump filter "port 80"
Filter By Source IP
> tcpdump filter "src x.x.x.x"
Filter By Destination IP
> tcpdump filter "dst x.x.x.x"
Filter By Host (src & dst) IP
> tcpdump filter "host x.x.x.x"
Filter By Host (src & dst) IP, excluding SSH traffic
> tcpdump filter "host x.x.x.x and not port 22"
C: pcap filter expression primitives include:
* rnr num
True if the packet was logged as matching the specified PF rule number (applies only to packets logged by OpenBSD's or FreeBSD's pf(4)).
* rulenum num
Synonymous with the rnr modifier.
References:
https://live.paloaltonetworks.com/t5/Management-Articles/How-To-Packet-Capture-tcpdump-On- Management-Interface/ta-p/55415
http://www.tcpdump.org/manpages/pcap-filter.7.html
NEW QUESTION: 2
DRAG DROP
Answer:
Explanation:
NEW QUESTION: 3
Why do you need to split a machine learning dataset into training data and test data?
A. To make sure your model is generalized for more than just the training data
B. So you can use one dataset for a wide model and one for a deep model
C. So you can try two different sets of features
D. To allow you to create unit tests in your code
Answer: A
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/