Explanation:
The dbutils.notebook.run() method is designed to run a notebook and return its exit value, starting an ephemeral job that runs immediately. It allows passing arguments to set widget values in the target notebook, making it the correct choice for this scenario. The timeout_seconds parameter controls the run's timeout, and the arguments parameter sets the widget values for processing.
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In a project involving multiple interdependent entities, a data engineer needs to call another notebook from the current one, passing arguments to set widget values for data processing. Which Python command accomplishes this?
A
dbutils.notebook.execute("path", timeout_seconds, {"argument1": "data1", "argument2": "data2"})
B
dbutils.notebook.run("path", timeout_seconds, {"argument1": "data1", "argument2": "data2"})
C
dbutils.notebook.trigger("path", timeout_seconds, {"argument1": "data1", "argument2": "data2"})
D
dbutils.notebook.call("path", timeout_seconds, {"argument1": "data1", "argument2": "data2"})
E
dbutils.notebook.implement("path", timeout_seconds, {"argument1": "data1", "argument2": "data2"})
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