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NEW QUESTION: 1
Study the log below and identify the scan type.
tcpdump -vv host 192.168.1.10
17:34:45.802163 eth0 < 192.168.1.1 > victim: ip-proto-117 0 (ttl 48, id 36166)
17:34:45.802216 eth0 < 192.168.1.1 > victim: ip-proto-25 0 (ttl 48, id 33796)
17:34:45.802266 eth0 < 192.168.1.1 > victim: ip-proto-162 0 (ttl 48, id 47066)
17:34:46.111982 eth0 < 192.168.1.1 > victim: ip-proto-74 0 (ttl 48, id 35585)
17:34:46.112039 eth0 < 192.168.1.1 > victim: ip-proto-117 0 (ttl 48, id 32834)
17:34:46.112092 eth0 < 192.168.1.1 > victim: ip-proto-25 0 (ttl 48, id 26292)
17:34:46.112143 eth0 < 192.168.1.1 > victim: ip-proto-162 0 (ttl 48, id 51058)
tcpdump -vv -x host 192.168.1.10
17:35:06.731739 eth0 < 192.168.1.10 > victim: ip-proto-130 0 (ttl 59, id 42060) 4500 0014 a44c 0000 3b82 57b8 c0a8 010a c0a8 0109 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000
A. nmap -sS 192.168.1.10
B. nmap -sV 192.168.1.10
C. nmap -sO -T 192.168.1.10
D. nmap -sR 192.168.1.10
Answer: C

NEW QUESTION: 2
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

NEW QUESTION: 3
CORRECT TEXT
Wireless network users recently began experiencing speed and performance issues after access point 2 (AP2) was replaced due to faulty hardware. The original network was installed according to a consultant's specifications and has always worked without a problem.
You a network technician, have been tasked with evaluating the situation and resolving the issues to improve both performance and connectivity. Refer to the following diagram and perform any Necessary changes to the wireless and wired infrastructure by adjusting devices.
NotE. Adjust the LEAST number of devices needed to fix the issue all blue icons in the image are clickable.



Answer:
Explanation:
AP 2 configuration is not correct.
The wireless mode should be 'G' and channel should be '11'.
For the wired interface, the speed should be set to 100 and duplex to 'Full'. Click 'Submit'.

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