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NEW QUESTION: 1
회사는 Amazon S3 버킷 내에 정적 웹 사이트를 호스팅합니다.이 웹 사이트는 전세계 수백만 명의 사용자를 지원할 것이며 회사는 데이터 전송 비용을 최소화하고자 합니다.
솔루션 설계자는 비용을 최소화하기 위해 무엇을 해야 합니까?
A. 교차 지역 복제를 사용하여 웹 사이트를 다른 지역의 추가 S3 버킷으로 복사하십시오.
B. S3 버킷을 원본 서버로 사용하여 Amazon CloudFront 배포판을 만듭니다.
C. 웹 사이트를 사용하기 위해 AWS Auto Scaling 그룹을 구현합니다.
D. 웹 사이트를 대규모 컴퓨팅 최적화 Amazon EC2 인스턴스로 이동하십시오.
Answer: B

NEW QUESTION: 2
Ann, a technician, connects a pair of switches using redundant links. When she checks the status of the links, one of them is not active, even when she changes ports. Which of the following MOST likely disabled the redundant connection to the other switch?
A. SSID mismatch
B. Port mirroring
C. IGRP routing
D. A
http://www.cisco.com/c/en/us/support/docs/lan-switching/spanning-tree-protocol/5234-5.html
E. Spanning tree
Answer: D

NEW QUESTION: 3
During the salvage of the Local Area Network and Servers, which of the following steps would normally be performed first?
A. Damage mitigation
B. Install LAN communications network and servers
C. Recover equipment
D. Assess damage to LAN and servers
Answer: D
Explanation:
The first activity in every recovery plan is damage assessment, immediately
followed by damage mitigation.
This first activity would typically include assessing the damage to all network and server
components (including cables, boards, file servers, workstations, printers, network equipment),
making a list of all items to be repaired or replaced, selecting appropriate vendors and relaying
findings to Emergency Management Team.
Following damage mitigation, equipment can be recovered and LAN communications network and
servers can be reinstalled.
Source: BARNES, James C. & ROTHSTEIN, Philip J., A Guide to Business Continuity Planning,
John Wiley & Sons, 2001 (page 135).

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

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