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NEW QUESTION: 1
You have an Azure Virtual Network named fabVNet with three subnets named Subnet-1, Subnet-2 and Subnet-3. You have a virtual machine (VM) named fabVM running in the fabProd service.
You need to modify fabVM to be deployed into Subnet-3. You want to achieve this goal by using the least amount of time and while causing the least amount of disruption to the existing deployment.
What should you do? To answer, drag the appropriate Power Shell cmdlet to the correct location in the Power Shell command. Each cmdlet may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation
NEW QUESTION: 2
Government data classifications include which of the following:(Choose four)
A. Top Secret
B. Confidential
C. Unclassified
D. Secret
E. Private
F. Open
Answer: A,B,C,D
Explanation:
One of the most common systems used to classify information is the one developed within the US Department of Defense. These include: unclassified, sensitive, confidential, secret, and top secret.
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. いいえ
B. はい
Answer: B
Explanation:
The two steps are present: process_step and train_step
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.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For 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=azure-ml-py