Answer-first summary for fast verification
Answer: run
The question asks to compare dataset1 (baseline) and dataset2 (new data) using the DataDriftDetector class. Based on the community discussion and Azure ML documentation, the 'run' method is specifically designed for ad-hoc comparison between two datasets to detect data drift. The 'backfill' method is used for analyzing historical data over time, not for direct comparison of two specific datasets. The 'get' method retrieves existing detector information, and 'update' modifies detector settings - neither performs the actual drift detection. The community consensus (with upvoted comments supporting A) and official documentation confirm that 'run' is the correct method for this scenario.
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You use Azure Machine Learning to train a model using a dataset named dataset1. You define a dataset monitor and create a new dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 using the Azure Machine Learning SDK for Python.
Which method of the DataDriftDetector class should you use?
A
run
B
get
C
backfill
D
update