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NEW QUESTION: 1
As a Scrum Master how will you coordinate with the Product Owner to ensure Value-Driven Delivery on Scrum projects?
A. By ensuring that requirements are prioritized based on opportunity for investment
B. By ensuring that customers realize the value at the end of project
C. By understanding what adds value to customers and ensuing that stakeholders are closely involved throughout the project
D. By ensuring that requirements are reprioritized after every Sprint
Answer: C
Explanation:
In order to provide Value-driven Delivery, it is important to meet a few requirements. First, those involved in a project should understand what adds value to customers and users, and should prioritize the high value requirements in the Prioritized Product Backlog. Second, those involved in a project should decrease uncertainty and constantly address risks that can potentially decrease value if they materialize; it is important to show product increments to project stakeholders at the end of each Sprint, enabling effective management of changes. Third, those involved in a project should create deliverables by producing potentially shippable, value-based product increments during each Sprint so that customers start realizing value early in the project.

NEW QUESTION: 2
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: 3
The NetCache appliance cannot be used for which of the following?
A. HTTP URL filtering
B. SMTP proxy
C. DNS caching
D. NFS caching
Answer: B