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NEW QUESTION: 1
You have 3 servers that runs Windows Server 2012 R2. The server contains the disks configured as shown in the following table.

You need to create a volume that can store up to 3 TB of user files.
The solution must ensure that the user files are available if one of the disks in the volume fails.
What should you create?
A. A mirrored volume on Disk l and Disk 4
B. Raid 5 Volume out of Disks 1, 2 and 3
C. A storage pool on Disk 1 and Disk 3
D. A mirrored volume on Disk 2 and Disk 3
E. A storage pool on Disk 2 and Disk 3
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Mirrored volume will be > 3Tb
Incorrect Answers:
A. Storage pool can't use Dynamic disk
C. Storage pool can't use Dynamic disk
D. is impossible, we need 3Tb of disk space
E. Raid5 need to be on dynamic disk

NEW QUESTION: 2
5,000のメールボックスを含むMicrosoft Exchange Server 2019組織があります。
Microsoft 365 E5サブスクリプションを購入してハイブリッド展開を作成します。 Exchange Onlineを指すようにすべてのMXレコードを構成します。
2,000のMicrosoft 365 E5ライセンスを購入し、2,000のメールボックスをExchange Onlineに移行します。
どの追加ライセンスを購入する必要があるのか​​を特定する必要があります。解決策はコストを最小限に抑える必要があります。
どのライセンスを確認しますか？
A. すべてのユーザー用のMicrosoft Azure Active DirectoryプレミアムP2ライセンス
B. オンプレミスユーザー用のMicrosoft Office 365 Enterprise E1ライセンス
C. Exchange Onlineユーザー用のEnterprise Mobility + Security E5ライセンス
D. 各オンプレミスメールボックスのExchange Online Protection（EOP）ライセンス
Answer: A
Explanation:
説明
参考文献：
https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/active-directory-whatis

NEW QUESTION: 3
群衆感情モデルの評価戦略を定義する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Step 1: Define a cross-entropy function activation
When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.
Step 2: Add cost functions for each target state.
Step 3: Evaluated the distance error metric.
References:
https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/