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NEW QUESTION: 1
A. Option A
B. Option C
C. Option E
D. Option B
E. Option D
Answer: A
Explanation:
Explanation
A RAT's installation mechanism is usually attached to a legitimate program. RATs are commonly hidden within games or other small programs, as well as in email attachments that users download. Imposters are able to customize RAT features, such as when and where to launch the Trojan. Intruders eventually trigger the server program on the victim's computer. Then, the RAT runs invisibly on the victim's computer and allows the intruder to gain remote access and control of the infected computer.
NEW QUESTION: 2
Answer:
Explanation:
Explanation
NEW QUESTION: 3
A customer is using the access point GUI to upgrade the software on an autonomous access point to convert it to a lightweight access point. Which two transfer protocols are valid for conducting this transfer from the WLC? (Choose two.)
A. RCP
B. TFTP
C. SFTP
D. HTTP
E. SCP
F. FTP
Answer: B,D
NEW QUESTION: 4
You have a Python data frame named salesData in the following format:
The data frame must be unpivoted to a long data format as follows:
You need to use the pandas.melt() function in Python to perform the transformation.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html
