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NEW QUESTION: 1
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION: 2
Contosoのストレージ要件を特定する必要があります。
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
Scenario: Move the existing product blueprint files to Azure Blob storage.
Scenario: Use unmanaged standard storage for the hard disks of the virtual machines.
Page blobs are optimized for writes at random locations within a blob. They also support Unmanaged Disks.
Scenario:
SQL Server Data Files in Microsoft Azure enables native support for SQL Server database files stored as blobs. It allows you to create a database in SQL Server running in on-premises or in a virtual machine in Microsoft Azure with a dedicated storage location for your data in Microsoft Azure Blob storage.
Box 2: No
Box 3: No
References:
https://docs.microsoft.com/en-us/sql/relational-databases/databases/sql-server-data-files-in-microsoft-azure
NEW QUESTION: 3
You have a SharePoint Server 2010 server farm that contains two web servers named Server1 and Server2 and a database server named Server2.
Server2 fails and cannot be restored from backup.
You need to add a new Web server named Server2 to the server farm.
What should you do first?
A. From Central Administration, configure the backup settings.
B. From Central Administration, remove Server2 from the farm.
C. At the command prompt, run psconfig.exe and specify the -cmd parameter.
D. At the command prompt, run stsadm.exe and specify the deleteadminvs operation.
Answer: B
Explanation:
Explanation/Reference:
BURGOS MNEMO: "cannot be restored from backup" = "remove Server"
NEW QUESTION: 4
Azure Machine Learning Studioで多重線形回帰モデルを作成しています。
いくつかの独立変数は高度に相関しています。
すべてのデータに対して効果的な特徴エンジニアリングを行うための適切な方法を選択する必要があります。
どの3つのアクションを順番に実行する必要がありますか？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領域に移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation:
Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform