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NEW QUESTION: 1
How can an administrator make the 'Search Vaults' button in Outlook available to users?
A. use the Exchange mailbox policy, 'Options' tab
B. use the Search Provisioning Group
C. use the Exchange desktop policy, 'Options' tab
D. use the IMAP Provisioning Group
Answer: D
Explanation:
Topic 12, Describe the process for importing and exporting archives
NEW QUESTION: 2
You have a server named FS1 that runs Windows Server 2012 R2.
You install the File and Storage Services server role on FS1.
From Windows Explorer, you view the properties of a shared folder named Share1 and you discover that the Classification tab is missing.
You need to ensure that you can assign classifications to Share1 from Windows Explorer manually.
What should you do?
A. Install the File Server Resource Manager role service.
B. Install the Enhanced Storage feature.
C. From Folder Options, clear Use Sharing Wizard (Recommend).
D. From Folder Options, select Show hidden files, folders, and drives.
Answer: A
Explanation:
On the Classification tab of the file properties in Windows Server 2012, File Classification Infrastructure adds the ability to manually classify files. You can also classify folders so that any file added to the classified folder will inherit the classifications of the parent folder.
Reference: What's New in File Server Resource Manager in Windows Server Your network contains an Active Directory forest. The forest contains two domains named contoso.com and fabrikam.com. The functional level of the forest is Windows Server 2003.
NEW QUESTION: 3
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、指定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。
履歴データに基づいて気象条件を予測するモデルを作成します。
データストアからデータを読み込み、処理されたデータを機械学習モデルのトレーニングスクリプトに渡すために、処理スクリプトを実行するパイプラインを作成する必要があります。
解決策：次のコードを実行します。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Explanation
Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu
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