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NEW QUESTION: 1
エンドポイントがIDグループから削除されたときにエンドポイントセッションの再認証を強制するには、Cisco ISEで何を設定する必要がありますか？
A. 姿勢評価
B. SNMPプローブ
C. 外部IDソース
D. CoA
Answer: A

NEW QUESTION: 2
可能なステップのリストを調べて、互換性レベル、キャラクタ・セット、およびエンディアン・フォーマットが同じプラットフォーム間で表領域を移送します。
1.ソース・データベースで表スペースを読み取り専用にします。
2.ソースデータベースからメタデータをエクスポートします。
3.メタデータをターゲットデータベースにインポートします。
4.ダンプファイルとデータファイルをターゲットマシンに転送します。
5. Recovery Manager（RMAN）を使用してデータファイルを変換します。
6.ターゲット・データベースで表スペースを読み書き可能にします。
必要な手順を正しい順序で指定します。 （最良の答えを選択する。）
A. 1, 5, 2, 4, 3, and 6
B. 2, 4, and 3
C. 2, 4, 3, and 5
D. 1, 2, 4, 3, and 6
Answer: D

NEW QUESTION: 3
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use K-means Clustering to detect faces in the pixels.
Answer: A
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION: 4
네트워크 공격의 증거를 수집하는데 사용할수 있는 것은 무엇입니까?
A. 액세스 제어 목록 (ACL)
B. Syslog보고
C. 바이러스 백신 프로그램
D. 침입 탐지 시스템 (IDS)
Answer: D
Explanation:
설명:
침입 탐지 시스템 (IDS)은 네트워크 공격의 증거를 수집하는데 사용됩니다.