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NEW QUESTION: 1
애플리케이션 컨테이너화의 모든 단계에서 데이터 무결성을 보존하는 가장 좋은 방법은 다음 중 어떤 것을 확인하는 것입니다.
A. 개발 팀은 애플리케이션 컨테이너의 정기적인 패치를 수행합니다.
B. 업무 분리는 애플리케이션 컨테이너 환경에서 개발되고 유지됩니다.
C. 개발자는 자신의 역할이 애플리케이션 보안 모범 사례와 어떤 관련이 있는지 교육을 받습니다.
D. 정보 보안 역할은 정보 보안 정책에서 정의되고 전달됩니다.
Answer: B
NEW QUESTION: 2
You are conducting a site survey for a customer that is about to deploy Cisco wireless phones.
Which three recommendations apply? (Choose three.)
A. The minimum recommended wireless signal strength for voice applications is -67 dBm.
B. The minimum recommended wireless signal strength for voice applications is -57 dBm.
C. Wireless cell overlap should be 10 percent.
D. The minimum recommended wireless signal strength for voice applications is -71 dBm.
E. Minimum SNR should be 15 dB.
F. Minimum SNR should be 25 dB.
G. Minimum SNR should be 35 dB.
H. Wireless cell overlap should be 20 percent.
Answer: A,F,H
NEW QUESTION: 3
Which backup level(s) does the EMC Avamar SQL Plug-in support?
A. Full only
B. Full, Differential, and Incremental
C. Full and Differential only
D. Full and Incremental only
Answer: B
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
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