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NEW QUESTION: 1
Which three attributes in the orchestration process definition utilize Oracle business rules to make runtime decisions for process behavior? (Choose three.)
A. Line Selection Criteria
B. Exit Criteria
C. Evaluation Sequence
D. Start After conditions
E. Cost of Change
Answer: B,D,E

NEW QUESTION: 2
What must you discuss with your customer before adding 14 rx2620 servers into a rack where six rx2660 servers are already built in?
A. additional power requirements
B. floor space requirements of the existing rack
C. physical security aspect of the data center
D. fire protection aspects of the environment
Answer: A

NEW QUESTION: 3
You have a sales system that contains the tables shown in the following table.

The Date table is marked as a date table.
DatelD is the date data type. You need to create an annual sales growth percentage measure.
Which DAX expression should you use?
A. (SUN(sales[sales_araount]) - CALCULATE(SUM(sales[sales_amount]), SAHEPERIOOLASTYEAR('Date'[DatelD]))/ CALCULATE(SUM(sales[sales_amount]), SAMEPERIOOLASTYEAR ('Date'[DateID])
B. CALCULATE(SUH(sales[sales_araount]), SAMEPERIODLASTYEAR('Date'[DatelD])
C. CALCULATE(SUM(sales[sales_amount]), DATESYTD('Date'[DatelD]))
D. SUM(sales[sales_amount]) - CALCULATE(SUM(sales[sales_amount]), SAMEPERIODLASTYEAR('Date'[DatelD])
Answer: B

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 E
B. Option B
C. Option C
D. Option A
E. Option D
Answer: A,E
Explanation:
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-