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NEW QUESTION: 1
An advantage of using central instead of local licensing is:
A. The license must be renewed when changing the IP address of a Security Gateway. Each module's license has a unique IP address.
B. Only one IP address is used for all licenses.
C. Licenses are automatically attached to their respective Security Gateways.
D. A license can be taken from one Security Management Server and given to another Security Management Server.
Answer: B

NEW QUESTION: 2
展示を参照してください。
IP SLA追跡エラーの2つの理由はどれですか？* （2つ選択してください）

A. 宛先はicmp-echoの172 30 30 2でなければなりません
B. しきい値が間違っています
C. デフォルトルートのネクストホップIPアドレスが間違っています
D. 送信元インターフェイスが正しく構成されていません
E. R1 LANネットワークに戻るルートがR2にありません
Answer: D,E

NEW QUESTION: 3
In the Cisco TelePresence Conductor web pages, which logs can you view?
A. event log and configuration log
B. system log, event log, and CDR log
C. system log and event log
D. system log, event log, configuration log, and CDR log
Answer: A

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 train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1:

Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability