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NEW QUESTION: 1
You have a computer named Computer1 that runs Windows 10.
Computer1 has the local users shown in the following table.

Which users can analyze the event logs on Computer1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

References:
https://docs.microsoft.com/en-us/windows/security/identity-protection/access-control/local-accounts#sec-localsy

NEW QUESTION: 2
A company's management is considering investing in a project with an expected life of 4 years. It has a positive net present value of $180,000 when cash flows are discounted at 8% per annum. The project's cash flows include a cash outflow of $100,000 for each of the four years. No tax is payable on projects of this type.
The percentage increase in the annual cash outflow that would cause the company's management to reject the project from a financial perspective is, to the nearest 0.1%:
A. 54.3%
B. 184.0%
C. 55,6%
D. 45.0%
Answer: A

NEW QUESTION: 3
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