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NEW QUESTION: 1
A senior incident response manager receives a call about some external IPs communicating with internal computers during off hours. Which of the following types of malware is MOST likely causing this issue?
A. Botnet
B. Armored virus
C. Ransomware
D. Polymorphic malware
Answer: A

NEW QUESTION: 2
When dual-link HSB is configured, an AP sets up CAPWAP links with both the active and standby ACs.
A. FALSE
B. TRUE
Answer: B

NEW QUESTION: 3
AdminA and AdminB are both logged in on SmartConsole. What does it mean if AdminB sees a locked icon
on a rule? Choose the BEST answer.
A. Rule is locked by AdminA, because an object on that rule is been edited.
B. Rule is locked by AdminA, because the save bottom has not been press.
C. Rule is locked by AdminA, and if the session is saved, rule will be available
D. Rule is locked by AdminA, and will make it available if session is published.
Answer: D

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins