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NEW QUESTION: 1
HOTSPOT


Answer:
Explanation:

NEW QUESTION: 2
A user asks a security practitioner for recommendations on securing a home network. The user recently purchased a connected home assistant and multiple IoT devices in an effort to automate the home. Some of the IoT devices are wearables, and other are installed in the user's automobiles. The current home network is configured as a single flat network behind an ISP- supplied router. The router has a single IP address, and the router performs NAT on incoming traffic to route it to individual devices.
Which of the following security controls would address the user's privacy concerns and provide the BEST level of security for the home network?
A. Install a firewall capable of cryptographically separating network traffic require strong authentication to access all IoT devices, and restrict network access for the home assistant based on time-of-day restrictions.
B. Segment the home network to separate network traffic from users and the IoT devices, ensure security settings on the home assistant support no or limited recording capability, and install firewall rules on the router to restrict traffic to the home assistant as much as possible.
C. Ensure all IoT devices are configured in a geofencing mode so the devices do not work when removed from the home network. Disable the home assistant unless actively using it, and segment the network so each IoT device has its own segment.
D. Change all default passwords on the IoT devices, disable Internet access for the IoT devices and the home assistant, obtain routable IP addresses for all devices, and implement IPv6 and IPSec protections on all network traffic.
Answer: B

NEW QUESTION: 3
提供されているトレーニングセットを使用して、バイナリ分類モデルを構築しています。
トレーニングセットは2つのクラス間で不均衡です。
データの不均衡を解決する必要があります。
この目標を達成するための3つの可能な方法は何ですか？各正解は完全なソリューションを示します注：
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. トレーニング機能セットを正規化します。
B. 分類にペナルティを科す
C. アンダーサンプリングまたはオーバーサンプリングを使用してデータセットをリサンプリングします
D. モデルの評価指標として精度を使用します。
E. マイノリティクラスで合成サンプルを生成します。
Answer: B,C,D
Explanation:
Explanation
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/