Azure Data Scientists Associate Practice Exam

When utilizing automated machine learning without preprocessing the data, which option should be set?

1. default

2. batch

3. off

4. none

The correct answer is: off

When using automated machine learning (AutoML) without preprocessing the data, setting the option to 'off' indicates that no additional preprocessing steps will be taken on the raw dataset. This choice signals to the AutoML system that it should work with the data as it is, without attempting any transformations or normalizations that would typically improve performance or model accuracy. The relevance of this setting becomes clear when recognizing that preprocessing can significantly alter the input features of a dataset. By opting for 'off', the practitioner might be aiming to analyze the raw performance of models directly on unmodified data. This could be important in situations where understanding the baseline model's performance on the original data is necessary, or when testing the assumptions of how preprocessing affects outcome might be a part of experimental analysis. In scenarios where preprocessing is set to 'default', 'batch', or 'none,' it implies either an automatic application of standard preprocessing methods or a miscommunication about the intended level of intervention on the raw data. Thus, setting the preprocessing to 'off' is key to ensuring that data is processed exactly in its original form, allowing for an unaltered evaluation of the models generated during training.