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NEW QUESTION: 1
Identify two features of Oracle BAM.
A. Enterprise Manager's monitoring capabilities are central to Oracle BAM's ability to monitor performance indicators.
B. You can analyze and present Information from activities spanning multiple systems and applications.
C. There is a built-in trouble ticket system to track assignment and completion of corrective actions.
D. Rich browser-based dashboards provide visibility into current values of key performance indicators (KPIs).
Answer: A,D
Explanation:
Note:
* Oracle Business Activity Monitoring (Oracle BAM) gives business executives the ability to monitor their business services and processes in the enterprise, to correlate KPIs down to the actual business process themselves, and most important, to change business processes quickly or to take corrective action if the business environment changes.

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu

NEW QUESTION: 3

A. Option A
B. Option B
C. Option D
D. Option C
Answer: B
Explanation:
Reference:
https://www.paloaltonetworks.com/documentation/80/panorama/panorama_adminguide/manage-firewalls/manag templates-and-template-stacks/configure-a-template-stack