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NEW QUESTION: 1

A. Option C
B. Option A
C. Option B
D. Option F
E. Option E
F. Option D
Answer: A,B,E,F

NEW QUESTION: 2
Which two characteristics apply to Cisco Catalyst 6500 Series Switch supervisor redundancy using NSF?
(Choose two.)
A. prevents route flapping
B. supports IPv4 and IPv6 multicast
C. uses the FIB table
D. supported by RIPv2, OSPF, IS-IS, and EIGRP
Answer: A
Explanation:
Explanation/Reference:
A key element of NSF is packet forwarding. In a Cisco networking device, packet forwarding is provided by Cisco Express Forwarding (CEF). CEF maintains the FIB, and uses the FIB information that was current at the time of the switchover to continue forwarding packets during a switchover. This feature reduces traffic interruption during the switchover.
During normal NSF operation, CEF on the active supervisor engine synchronizes its current FIB and adjacency databases with the FIB and adjacency databases on the redundant supervisor engine. Upon switchover of the active supervisor engine, the redundant supervisor engine initially has FIB and adjacency databases that are mirror images of those that were current on the active supervisor engine. For platforms with intelligent modules, the modules will maintain the current forwarding information over a switchover.
For platforms with forwarding engines, CEF will keep the forwarding engine on the redundant supervisor engine current with changes that are sent to it by CEF on the active supervisor engine. The modules or forwarding engines will be able to continue forwarding after a switchover as soon as the interfaces and a data path are available.
As the routing protocols start to repopulate the RIB on a prefix-by-prefix basis, the updates will cause prefix-by-prefix updates to CEF, which it uses to update the FIB and adjacency databases. Existing and new entries will receive the new version ("epoch") number, indicating that they have been refreshed. The forwarding information is updated on the modules or forwarding engine during convergence. The supervisor engine signals when the RIB has converged. The software removes all FIB and adjacency entries that have an epoch older than the current switchover epoch. The FIB now represents the newest routing protocol forwarding information.

NEW QUESTION: 3
Azure Machine Learning Studioを使用してデータセットを分析しています。
各機能列のp値と一意の値カウントを含む統計サマリーを生成する必要があります。
ユーザーはどちらのモジュールを使用できますか？それぞれの正解は完全な解決策を提示します。
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。
A. 線形相関の計算
B. インジケーター値に変換
C. カウントテーブルのエクスポート
D. Pythonスクリプトの実行
E. データの要約
Answer: A,C
Explanation:
Explanation
The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer (deprecated) modules.
E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:
How many missing values are there in each column?
How many unique values are there in a feature column?
What is the mean and standard deviation for each column?
The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data