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NEW QUESTION: 1
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.
A. Option C
B. Option B
C. Option A
D. Option D
E. Option E
Answer: D,E
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
NEW QUESTION: 2
Answer:
Explanation:
Explanation
NEW QUESTION: 3
A. Option A
B. Option C
C. Option D
D. Option B
Answer: A
NEW QUESTION: 4
A. 10.154.154.1
B. 192.168.5.3
C. 172.16.5.1
D. 10.1.1.2
Answer: C
Explanation:
The highest IP address of all loopback interfaces will be chosen -> Loopback 0 will be chosen as the router ID.