DP-100 Question #139: Real Exam Question with Answer & Explanation

Question

You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code: from azureml.pipeline.core import Pipeline from azureml.core.experiment import Experiment pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline) You need to monitor the progress of the pipeline execution. What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

• ARun the following code in a notebook:
• BUse the Inference Clusters tab in Machine Learning Studio.
• CUse the Activity log in the Azure portal for the Machine Learning workspace.
• DRun the following code in a notebook:
• ERun the following code and monitor the console output from the PipelineRun object:

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) https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step#monitor- the-parallel-run-job

No community discussion yet for this question.