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NEW QUESTION: 1
You have decided to onboard your user data via the Offline Match Integration. You are in the process of determining the record format in the data file.
Which record format from the following options is valid?
A. awytM3DD bk112=25,bk111=M,bk115=2
B. awytM3DD bk112=25~bk111= M~bk115=2
C. awytM3DD bk112=25#bk111= M#bk115=2
D. awytM3DD bk112=25%bk111=M%bk115=2
E. awytM3DD bk112=25@bk111=M@bk115=2
F. awytM3DD bk112=25|bk111=M|bk115=2
Answer: F

NEW QUESTION: 2
CPE is:
A. Owned or leased by the end user.
B. Owned by the ILEC.
C. Provided by the IXC.
D. Owned by a CLEC.
Answer: A

NEW QUESTION: 3

A. Option B
B. Option E
C. Option D
D. Option C
E. Option F
F. Option A
Answer: A,C,E

NEW QUESTION: 4
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 B
B. Option D
C. Option E
D. Option C
E. Option A
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)

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