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NEW QUESTION: 1
会社は、Internet Information Server（IIS）を実行するAzure仮想マシンスケールセット（VMSS）を使用して複数のWebサイトをホストしています。
すべてのネットワーク通信は、エンドツーエンドのSecure Socket Layer（SSL）暗号化を使用して保護する必要があります。ユーザーセッションは、Cookieベースのセッションアフィニティを使用して同じサーバーにルーティングする必要があります。
表示されている画像は、VMSSへのWebサイトのネットワークトラフィックフローを示しています。

ドロップダウンメニューを使用して、各質問に回答する回答選択肢を選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Azure Application Gateway
You can create an application gateway with URL path-based redirection using Azure PowerShell.
Box 2: Path-based redirection and Websockets
Reference:
https://docs.microsoft.com/bs-latn-ba/azure//application-gateway/tutorial-url-redirect-powershell

NEW QUESTION: 2
For S5000T storage array, if a virtual snapshot of the source LUN vested in the controller A, then the virtual resource pool A snapshot can only be used in the resource LUN.
A. False
B. True
Answer: A

NEW QUESTION: 3
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 MimicExplainer.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.
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