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NEW QUESTION: 1
Drag and drop the characteristics from the left onto the routing protocols they describe on the right.

Answer:
Explanation:

NEW QUESTION: 2
Quality assurance
A. Both 1)is the technical process that include the construction of control charts which specify acceptability limits for conforming output, and 2) is an auditing function that provides feedback to the project team and client about the quality of output being produced.
B. refers to the prevention of product defects
C. is an auditing function that provides feedback to the project team and client about the quality of output being produced.
D. is the technical process that include the construction of control charts which specify acceptability limits for conforming output.
E. Both 1) refers to the prevention of product defects, and 2)is an auditing function that provides feedback to the project team and client about the quality of output being produced.
Answer: C

NEW QUESTION: 3
You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?
A. run.tag('row_count', rows)
B. run.log('row_count', rows)
C. run.upload_file('row_count', './data.csv')
D. run.log_table('row_count', rows)
E. run.log_row('row_count', rows)
Answer: B
Explanation:
Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Incorrect Answers:
E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.
Example: run.log_row("Y over X", x=1, y=0.4)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run