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You need to implement a model development strategy to determine a user's tendency to respond to an ad. Which technique should you use?
Correct Answer: A
Split Data partitions the rows of a dataset into two distinct sets. The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression. Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date. Scenario: Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement. The distribution of features across training and production data are not consistent Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data