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NEW QUESTION: 1
What Security Access Manager pdadmin command will create an object named /Management/application-object that is an application container object (type 14)?
A. createobject /Management/application-object type 14
B. object create /Management/application-object "Application Object" 14 ispolicyattachable yes
C. object add /Management/application-object "Application Object" 14 ispolicyattachable yes
D. objectspace create /Management/application-object type 14 ispolicyattachable true
Answer: B

NEW QUESTION: 2
What three statements are true about LIF failover? (Choose three.)
A. A cluster management Ethernet LIF can fail over when a port is down.
B. A data Ethernet LIF can fail over when the cluster is in quorum.
C. A node management Ethernet LIF can fail over to its partner node when a port is down.
D. A cluster management Ethernet LIF will never automatically revert back to its home port
E. A node management Ethernet LIF will never automatically revert back to its home port.
F. A data Ethernet LIF will not automatically revert back to its home port.
Answer: B,E,F

NEW QUESTION: 3
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 A
B. Option E
C. Option D
D. Option B
E. Option 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