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NEW QUESTION: 1

What is the result?
A. message = Hi XvXryonX!
B. message = Hi Xveryone!
C. message = Hi everyone!
D. message
E. A compile time error is produced.
F. A runtime error is produced.
Answer: A

NEW QUESTION: 2
Which two steps must an administrator take to create an approval policy through two levels of management when expanding the resources of a virtual machine beyond a specific footprint? (Choose two.)
A. Attach the approval policy to the specific day 2 actions: increase RAM, add CPUs and add Disk storage.
B. Set up a conditional approval policy.
C. Configure the approval policy to re-provision the virtual machine based on a larger blueprint if approval is granted at all levels.
D. Attach a multiple layer approval policy to the service to which the blueprint is attached.
E. Create a two layer approval policy.
Answer: B,C

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A
B. オプションD
C. オプションE
D. オプションB
E. オプションC
Answer: B,C
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-job