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NEW QUESTION: 1
HOTSPOT




Answer:
Explanation:
Box 1: make a shadow copy of the message on another server
Box 2: two days

NEW QUESTION: 2

A. Option C
B. Option B
C. Option A
D. Option D
Answer: A

NEW QUESTION: 3
Ihr Netzwerk enthält eine Active Directory-Domäne. Die Domäne enthält einen neuen Dateiserver mit dem Namen Server1, auf dem eine Server Core-Installation von Windows Server 2016 ausgeführt wird.
Server1 verfügt über ein mit ReFS formatiertes Volume D: und ein mit NTFS formatiertes Volume E: Die Volumes enthalten keine Daten.
Sie installieren den Datendeduplizierungs-Rollendienst auf Server1.
Sie müssen die Datendeduplizierung für Volumes auf D: und E: implementieren.
Lösung: In Windows PowerShell führen Sie Format-Volume E: -FileSystem ReFS und Enable-DeDupVolume -Volume D:, E: aus.
Erfüllt dies das Ziel?
A. Nein
B. Ja
Answer: B
Explanation:
Erläuterung
Verweise: https://docs.microsoft.com/en-us/windows-server/storage/data-deduplication/understand

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