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NEW QUESTION: 1
Which statement is true about a Data Center Interconnect over an IP network?
A. Layer 2 data must be encapsulated.
B. Layer 3 data must be encapsulated.
C. Layer 2 data must traverse an MPLS LSP.
D. Layer 2 data must traverse a point-to-point link.
Answer: C
NEW QUESTION: 2
Disclaimers and assumptions are a part of
A. Research Notes
B. None of the above
C. Wealth Plan
D. Data gathering
Answer: C
NEW QUESTION: 3
HOTSPOT
An organization has over 10,000 users and uses a SQL-based Active Directory Federation Services (AD FS) 2.0 server farm.
You need to change the AD FS service account password.
What should you do? Select the correct answer from each list in the answer area.
Hot Area:
Answer:
Explanation:
Explanation/Reference:
Explanation:
We must update the domain password for the AD FS service account in Active Directory Domain Services (AD DS) and then update the AD FS AppPool and the AD FS service account on all federation servers in the federation server farm to mirror the new domain password.
The AD FS AppPool is configured through Internet Information Services (IIS) Manager.
The AD FS Windows Service Properties is configured through the Windows Services snap-in.
References:
https://technet.microsoft.com/en-us/library/hh344806%28v=ws.10%29.aspx
NEW QUESTION: 4
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックス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: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
