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NEW QUESTION: 1
Which three statements are true about System Global Area (SGA)?
A. Each server process has its own SGA.
B. SGA is allocated automatically when a database is mounted.
C. SGA contains shared memory structures for all Oracle processes running on a single server.
D. SGA is used to speed up queries by caching data blocks, program data, information about database objects, and query results
E. SGA is allocated automatically when a database is started.
F. SGA is read and written by server and background processes.
Answer: D,E,F
NEW QUESTION: 2
An administrator needs to save the nightly QRadar backups on a network storage.
The administrator has established the connection to the network storage.
What should the administrator do next?
A. Change the Backup Repository Path by adding a new Network Activity Rule.
B. Change the Backup Repository Path to the network storage location using the System Settings window.
C. Change the Backup Repository Path to the network storage location using the Backup Recovery Configuration window.
D. Configure the new network storage using the Assets Manager
Answer: C
Explanation:
Reference:
http://ftpmirror.your.org/pub/misc/ftp.software.ibm.com/software/security/products/qradar/ documents/7.2.8/en/b_qradar_admin_guide.pdf (146)
NEW QUESTION: 3
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アンピボットする必要があります。
変換を実行するには、Pythonで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、回答領域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