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NEW QUESTION: 1
次の表に示すユーザーを含むcontoso.comという名前のAzure Active Directory(Azure AD)テナントがあります。

Azure AD Privileged Identity Management (PIM) is enabled for the tenant.
In PIM, the Password Administrator role has the following settings:
* Maximum activation duration (hours): 2
* Send email notifying admins of activation: Disable
* Require incident/request ticket number during activation: Disable
* Require Azure Multi-Factor Authentication for activation: Enable
* Require approval to activate this role: Enable
* Selected approver: Group1
You assign users the Password Administrator role as shown in the following table.

以下の各ステートメントについて、ステートメントが真である場合は「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には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Yes
Active assignments don't require the member to perform any action to use the role. Members assigned as active have the privileges assigned to the role at all times.
Box 2: No
MFA is disabled for User2 and the setting Require Azure Multi-Factor Authentication for activation is enabled.
Note: Eligible assignments require the member of the role to perform an action to use the role. Actions might include performing a multi-factor authentication (MFA) check, providing a business justification, or requesting approval from designated approvers.
Box 3: Yes
User3 is Group1, which is a Selected Approver Group
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/active-directory/privileged-identity-management/pim-resource-roles-

NEW QUESTION: 2
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、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、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。
あなたの会社には、パリのオフィスに100人のユーザーがいます。
オンプレミスネットワークには、次の表に示すサーバーが含まれます。

新しいサブスクリプションを作成します。すべてのサーバーをAzureに移動する必要があります。
解決策:azcopy.exeを実行します。
これは目標を達成していますか?
A. いいえ
B. はい
Answer: A

NEW QUESTION: 3
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Number of nodes in each hidden layer
B. Weights
C. Number of hidden layers
D. Biases
Answer: A,C
Explanation:
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview


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