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NEW QUESTION: 1
Welche der folgenden Aussagen beschreibt NICHT, wie das Testen zu einer höheren Qualität beiträgt?
A. Eine Überprüfung der Anforderungsspezifikationen vor der Implementierung des Systems kann die Qualität verbessern
B. Softwaretests identifizieren Fehler, die zur Verbesserung der Entwicklungsaktivitäten verwendet werden können.
C. Richtig gestaltete Tests, die bestanden werden, reduzieren das Risiko in einem System
D. Das Testen von Software zeigt die Abwesenheit von Fehlern
Answer: D

NEW QUESTION: 2

A. authentication order mab dot1x
B. authentication order dot1x mab
C. authentication open
D. dot1x timeout tx-period
E. mab
F. no authentication timer
Answer: A,E

NEW QUESTION: 3
What is the correct order of steps to perform an online database backup?
a: alter database begin backup;
b: alter database end backup;
c: Back up the database datafiles.
d: Back up the archive log files.
e:
alter system switch logfile;
A. a, c, b, e, d
B. a, c, b, d, e
C. d, b, c, a, e
D. e, d, a, b, c
E. a, b, c, d, e
Answer: A
Explanation:
(link)
To back up online read/write tablespaces in an open database:
1.Use the DBA_DATA_FILES data dictionary view to identify all of the data files in the tablespace.
2.ALTER TABLESPACE users BEGIN BACKUP;
3.Back up the online data files of the online tablespace with operating system commands.
4.ALTER TABLESPACE users END BACKUP;
5.ALTER SYSTEM ARCHIVE LOG CURRENT; Archive the unarchived redo logs so that the redo required to recover the tablespace backup is archived.

NEW QUESTION: 4
次の形式のsalesDataという名前のPythonデータフレームがあります。
データフレームは、次のように長いデータ形式にピボット解除する必要があります。
変換を実行するには、Pythonのpandas.melt（）関数を使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

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