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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution.
Determine whether the solution meets the stated goals.
You have the following resource groups:
Developers must connect to DevServer only through DevWorkstation. To maintain security, DevServer must not accept connections from the internet.
You need to create a private connection between the DevWokstation and DevServer.
Solution: Configure a public IP address on DevServer_WestCentral. Configure the Network Security Group to allow all incoming ports.
Does the solution meet the goal?
A. Yes
B. NO
Answer: A
NEW QUESTION: 2
You have a SharePoint Server 2010 server farm. The farm contains two Web servers.
The load between the two Web servers is distributed by using DNS round robin.
You need to ensure that the farm is available for all users if one of the web servers fails.
What should you do?
A. Add a new Web server to the farm.
B. Configure alternate access mappings.
C. Implement Windows Network Load Balancing.
D. Configure netmask ordering.
Answer: C
NEW QUESTION: 3
Which foundational business performance metrics are derivable from business architecture?
A. Efficiency, effectiveness and impact
B. Automation level, process alignment and effectiveness
C. Effectiveness, impact and breadth of coverage
D. Efficiency, effectiveness and automation level
Answer: C
NEW QUESTION: 4
フォルダーを参照するcsvjolderという名前のファイルデータセットを登録します。フォルダーには、AzureストレージBLOBコンテナー内の複数のコンマ区切り値（CSV）ファイルが含まれています。次のコードを使用して、ファイルデータセットからデータをロードするスクリプトを実行する予定です。次の変数を作成してインスタンス化します。
次のコードがあります。
スクリプトが参照するファイルをスクリプトが確実に読み取れるようにするには、データセットを渡す必要があります。コードコメントを置き換えるには、どのコードセグメントを挿入する必要がありますか？
A）
B）
C）
D）
A. オプションA
B. オプションB
C. オプションC
D. オプションD
Answer: D
Explanation:
Explanation
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml