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NEW QUESTION: 1
Which statement regarding migration from DB2 10 for z/OS to DB2 11 for z/OS is true?
A. All packages last bound prior to DB2 9 for z/OS must be rebound in DB2 10 or DB2 11.
B. All packages must be rebound with application compatibility set to V10R1.
C. The size of the DB2 bootstrap datasets must be increased to accommodate a larger LRSN and RBA length.
D. The DB2 catalog and directory must be changed to reside on SMS managed DASD.
Answer: B

NEW QUESTION: 2
Which statement about technology implementation strategy is true?
A. Cisco Unified Communications Manager Express in SRST mode can be configured to function with no Cisco Unified Communications Manager cluster in the enterprise.
B. SRST and MGCP fallback can be configured to function with no Cisco Unified Communications Manager cluster in the enterprise.
C. Cisco Unified Communications Manager Express can be configured to function with no Cisco
D. SRST can be configured to function with no Cisco Unified Communications Manager cluster in the enterprise.
Answer: C
Explanation:
Explanation/Reference:
Explanation:

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
Which two options ate common PTP device types? {Choose two )
A. sundials
B. boundary clock
C. transparent clock
D. crystal clock
E. network clock
Answer: B,C