You write a Python script that processes data in a comma-separated values (CSV) file. You plan to run this script as an Azure Machine Learning experiment. The script loads the data and determines the

Question

Exhibit

Options

• Arun.upload_file(row_count', ./data.csv')
• Brun.log(`row_count', rows)
• Crun.tag(`row_count', rows)
• Drun.log_table(`row_count', rows)
• Erun.log_row(`row_count', rows)

• A
  3% (1)
• B
  91% (32)
• C
  6% (2)

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 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 Example: run.log_row("Y over X", x=1, y=0.4) https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run

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