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NEW QUESTION: 1
Which Cisco Unified Presence operation mode supports the highest number of users for large-scale Cisco Unified CVP solutions?
A. Microsoft Office Interoperability
B. Microsoft Active Directory
C. Cisco Unified Presence
D. SIP Proxy
E. Cisco Unified Communications
Answer: D
NEW QUESTION: 2
You are preparing computers for a new ClusterXL deployment. For your cluster, you plan to use three machines with the following configurations:
Cluster Member 1: OS - GAiA; NICs - QuadCard; Memory - 1 GB; Security Gateway - version: R71 and primary Security Management Server installed, version: R77
Cluster Member 2: OS - GAiA; NICs - 4 Intel 3Com; Memory - 1 GB; Security Gateway only, version: R77 Cluster Member 3: OS - GAiA; NICs - 4 other manufacturers; Memory - 512 MB; Security Gateway only, version: R77
Are these machines correctly configured for a ClusterXL deployment?
A. No, Cluster Member 3 does not have the required memory.
B. No, the Security Gateway cannot be installed on the Security Management Server.
C. Yes, these machines are configured correctly for a ClusterXL deployment.
D. No, the Security Management Server is not running the same operating system as the cluster members.
Answer: B
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
NEW QUESTION: 4
Sie verwenden die Nachrichtenermittlung mit der Bedingungstechnik. Welche Belegfelder können Sie in SAP Process Management in diesen Prozess einbeziehen?
Es gibt 2 richtige Antworten auf diese Frage.
A. Pflanze
B. Buchungskreis
C. Einkaufsgruppe
D. Materialgruppe
Answer: A,B