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NEW QUESTION: 1
カスタマーネットワークでEIGRPがどのように機能しているかを評価するように求められました。







R4とR2間のEIGRP隣接関係の認証に使用されているキーチェーンはどれですか？
A. シスコ
B. EIGRP
C. キー
D. MD5
Answer: A
Explanation:
説明
R4およびR2の構成は次のとおりです。

明らかに、実際のキーチェーンの名前はCISCOです。

NEW QUESTION: 2
Which risk could lead to a lengthy farm outage in the event of a corrupt SQL data store?
A. An untested SQL backup and restoration process
B. SQL server backups are encrypted
C. The use of SQL Mirroring instead of SQL AlwaysOn Availability Groups
D. SQL servers that are NOT running on the same service packs
Answer: A

NEW QUESTION: 3
Is this an expected behavior when an incremental import is performed from Active Directory?
Solution: Only the groups are imported
A. No
B. Yes
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:
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