Answer-first summary for fast verification
Answer: run
The question asks to compare dataset1 (baseline) and dataset2 (new data) using the DataDriftDetector class. The run method is specifically designed for ad-hoc comparison between two datasets, making it the correct choice. Community discussion supports this with the highest upvoted comments (3 and 2 upvotes) explaining that 'run' executes the data drift detection process between baseline and target datasets. The backfill method (option C) is incorrect as it's used for historical analysis over time periods, not direct dataset comparison. Options B (get) and D (update) are unrelated to data drift detection operations.
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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