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NEW QUESTION: 1
To support the deployment of a new provisioning system, a company wants to install and configure a production domain with Oracle SOA Suite and Oracle Service Bus.
Which statement is true about the required effort?
A. The installation of Oracle SOA Suite includes the installation of Oracle Service Bus.
B. Separate domains must be configured for Oracle SOA Suite and Oracle Service Bus.
C. The Oracle Repository Creation Utility (RCU) must be run twice to create the required schemas.
D. A supported version of WebLogic Server must be installed before installing Oracle SOA Suite.
Answer: C

NEW QUESTION: 2
Dynamics 365 SalesとMicrosoft 365の両方を使用するPower Appsアプリを構築しています。
次の各ステートメントについて、ステートメントがtrueの場合は[はい]を選択します。それ以外の場合は、[いいえ]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: No
When you offer your application for use by other companies through a purchase or subscription, you make your application available to customers within their own Azure tenants. This is known as creating a multi-tenant application.
Box 2: No
Reference:
https://docs.microsoft.com/en-us/azure/active-directory/manage-apps/isv-sso-content

NEW QUESTION: 3
Annual Loss Expectancy is derived from the function of which two factors?
A. Single Loss Expectancy and Exposure Factor
B. Annual Rate of Occurrence and Asset Value
C. Annual Rate of Occurrence and Single Loss Expectancy
D. Safeguard Value and Annual Rate of Occurrence
Answer: C

NEW QUESTION: 4
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
ロジスティック回帰アルゴリズムを使用して分類モデルをトレーニングします。
各機能の重要度を、全体的なグローバル相対重要度値として、および特定の予測セットのローカル重要度の尺度として計算することにより、モデルの予測を説明できる必要があります。
必要なグローバルおよびローカルの特徴重要度値を取得するために使用できる説明を作成する必要があります。
解決策：MimicExplainerを作成します。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
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