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NEW QUESTION: 1
What is the standard Layer 3 DSCP media packet value that should be set for Cisco
TelePresence endpoints?
A. CS3 (24)
B. CS4 (32)
C. AF41 (34)
D. EF (46)
Answer: B

NEW QUESTION: 2
Refer to the exhibit.

Cisco 642-437 Exam
Which three commands need to be entered so that IP phones on the Voice VLAN 120 will receive an IP address from the DHCP server that is located at 10.1.5.2? (Choose three.)
A. interface voice vlan 120
B. ip helper-address 10.1.5.2
C. interface vlan 120
D. ip address 10.1.120.0/24
E. ip address 10.1.120.1 255.255.255.0
Answer: B,C,E

NEW QUESTION: 3
A wireless engineer must implement a corporate wireless network for a large company in the most efficient way possible. The wireless network must support 32 VLANs for 300 employees in different departments.
Which solution must the engineer choose?
A. Configure a second WLC to support half of the APs in the deployment.
B. Configure 16 WLANs to be broadcast on the 2.4-GHz band and 16 WLANs to be broadcast on the
5.0-GHz band.
C. Configure different AP groups to support different VLANs, so that all of the WLANs can be broadcast on both radios.
D. Configure one single SSID and implement Cisco ISE for VLAN assignment according to different user roles.
Answer: D
Explanation:

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