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NEW QUESTION: 1
효과적인 취약성 관리 프로세스를 생성하려면 다음 단계를 따르십시오.

Answer:
Explanation:

설명

NEW QUESTION: 2
Ihr Kunde möchte eine Abfrage mit einer externen URL verknüpfen. Welche der folgenden Schritte sind für eine Einstellung der Berichtsschnittstelle erforderlich?
Hinweis: Auf diese Frage gibt es 3 richtige Antworten.
A. Wählen Sie auf der Registerkarte Quellsysteme ein System aus.
B. Wählen Sie Empfängerobjekt und geben Sie die Webadresse ein.
C. Fügen Sie die Webadresse in die Zuordnungsdetails ein.
D. Wählen Sie die Webadresse aus der Liste der Berichtstypen aus.
E. Fügen Sie den Parameter für die Webadresse in die Zuordnungsdetails ein.
Answer: B,D,E

NEW QUESTION: 3
Azure ML SDKを使用してバッチ推論パイプラインを作成します。次のコードを使用してパイプラインを実行します。
azureml.pipeline.coreからのインポートパイプライン
azureml.core.experiment import Experimentから
pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)
パイプライン実行の進行状況を監視する必要があります。
この目標を達成するための2つの可能な方法は何ですか？それぞれの正解は完全なソリューションを示します。
注：それぞれの正しい選択は1ポイントの価値があります。

A. オプションD
B. オプションC
C. オプションB
D. オプションA
E. オプションE
Answer: A,E
Explanation:
Explanation
A batch inference job can take a long time to finish. This example monitors progress by using a Jupyter widget. You can also manage the job's progress by using:
* Azure Machine Learning Studio.
* Console output from the PipelineRun object.
from azureml.widgets import RunDetails
RunDetails(pipeline_run).show()
pipeline_run.wait_for_completion(show_output=True)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step#monitor-the-parallel-run-