Answer-first summary for fast verification
Answer: No
The solution does NOT meet the goal because it only provides the filename 'dataset1.csv' without specifying the full path to the dataset. In Azure Machine Learning command jobs, when passing data paths as parameters, you need to provide the complete path where the dataset is mounted or accessible within the job execution context. The community discussion shows a split opinion, but the technically correct answer is 'No' (B) - with 67% of users selecting A and 33% selecting B. However, the detailed reasoning from commenters like avinyc and colin1919 (who received upvotes) correctly points out that the path is not properly specified. The correct approach in Azure ML would be to use parameter references like `${{inputs.training_data}}` or provide the full mounted path where the dataset is accessible.
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You are managing an Azure Machine Learning workspace. A Python script named script.py reads an argument called training_data, which specifies the path to a training data file named dataset1.csv.
You plan to execute script.py as a command job to train a machine learning model. You must provide the command to pass the dataset's file path as a parameter value when submitting the script as a training job.
The proposed solution is:
python script.py --training_data dataset1.csv
python script.py --training_data dataset1.csv
Does this solution meet the goal?
A
Yes
B
No