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NEW QUESTION: 1
You suspect that multiple unrelated security events have occurred on several nodes on a corporate network.
You must review all logs and correlate events when necessary to discover each security event by clicking on each node. Only select corrective actions if the logs shown a security event that needs remediation. Drag and drop the appropriate corrective actions to mitigate the specific security event occurring on each affected device.
Instructions:
The Web Server, Database Server, IDS, Development PC, Accounting PC and Marketing PC are clickable.
Some actions may not be required and each actions can only be used once per node. The corrective action order is not important. If at any time you would like to bring back the initial state of the simulation, please select the Reset button. When you have completed the simulation, please select the Done button to submit.
Once the simulation is submitted, please select the Next button to continue.


Answer:
Explanation:

NEW QUESTION: 2

A. Option C
B. Option B
C. Option D
D. Option E
E. Option A
Answer: A,E

NEW QUESTION: 3
機械学習タスクを適切なシナリオに一致させます。
答えるには、適切なタスクを左側の列から右側のシナリオにドラッグします。各タスクは、1回使用することも、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。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

NEW QUESTION: 4
Given the code fragment:
for (int ii = 0; ii < 3;ii++) {
int count = 0;
for (int jj = 3; jj > 0; jj--) {
if (ii == jj) {
++count;
break;
} }
System.out.print(count);
continue;
}
What is the result?
A. 0
B. 012
C. 000
D. 011
Answer: D