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NEW QUESTION: 1
Where is the best placement of the Rendezvous Point in a PIM SSM network?
A. The Rendezvous Point is not required in a PIM SSM network
B. The Rendezvous Point can be positioned at any router without impact
C. In the redundant core
D. As close as possible to the multicast source
E. As close as possible to the receiver
Answer: A

NEW QUESTION: 2
The OPTIMIZE TABLE command should be used...
A. To improve performance by sorting indexes.
B. To increase performance by defragmenting the table.
C. To correct problems with a MyISAM table that have become corrupted.
D. To check a tables structure to see if it may have been damaged and needs repair.
E. To improve performance by updating index statistics.
Answer: A,B,E
Explanation:
30.2.4. OPTIMIZE TABLE The OPTIMIZE TABLE statement cleans up a MyISAM table by defragmenting it. This involves reclaimingunused space resulting from deletes and updates, and coalescing records that have becomesplit and storednon-contiguously. OPTIMIZE TABLE also sorts the index pages if they are out of order and updates the indexstatistics.

NEW QUESTION: 3
ネットワークにcontoso.comという名前のActive Directoryフォレストが含まれています。
パートナー企業にはfabrikam.comという名前の森があります。 各フォレストには1つのドメインがあります。
fabrikam.comのResearchという名前のグループにcontoso.comのリソースへのアクセスを提供する必要があります。 ソリューションは最小特権の原則を使用する必要があります。
あなたは何をするべきか？
A. 選択的認証を使用するfabrikam.comからcontoso.comへの一方向フォレストの信頼を作成します。
B. fabrikam.comからcontoso.comへの外部信頼を作成します。 fabrikam.comでActive Directory分割許可を有効にします。
C. 選択認証を使用するcontoso.comからfabrikam.comへの一方向フォレストの信頼を作成します。
D. contoso.comからfabrikam.comへの外部信頼を作成します。 contoso.comでActive Directory分割のアクセス許可を有効にします。
Answer: C

NEW QUESTION: 4
クリーニングが必要な生のデータセットを分析しています。
Azure Machine Learning Studioを使用して、変換と操作を実行する必要があります。
変換を実行するには、正しいモジュールを識別する必要があります。
どのモジュールを選択する必要がありますか？答えるには、適切なモジュールを正しいシナリオにドラッグします。各モジュールは、1回、複数回使用することも、まったく使用しないこともできます。
コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
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:

説明

Box 1: Clean Missing Data
Box 2: SMOTE
Azure Machine Learning StudioのSMOTEモジュールを使用して、機械学習に使用されるデータセット内の過小評価されたケースの数を増やします。 SMOTEは、既存のケースを単純に複製するよりも、まれなケースの数を増やすより良い方法です。
ボックス3：インジケーター値に変換
Azure Machine Learning Studioのインジケーター値に変換モジュールを使用します。このモジュールの目的は、カテゴリー値を含む列を、機械学習モデルの機能としてより簡単に使用できる一連のバイナリインジケーター列に変換することです。
ボックス4：重複する行を削除する
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values