Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You work as a data scientist for a large hotel chain and have been asked to assist the marketing team in developing a targeted marketing strategy. The goal is to make predictions about user lifetime value (LTV) over the next 20 days so that marketing efforts can be adjusted accordingly. The customer dataset is stored in BigQuery, and you are preparing this tabular data for training with Google Cloud's AutoML Tables. The dataset contains multiple columns that have time-related information, rather than a single unified time column. How should you proceed to ensure that AutoML builds the most accurate model based on your data?
A
Manually combine all columns that contain time-related information into an array. Allow AutoML to interpret this array appropriately. Choose an automatic data split across the training, validation, and testing sets.
B
Submit the data for training without performing any manual transformations. Allow AutoML to handle the appropriate transformations. Choose an automatic data split across the training, validation, and testing sets.
C
Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column. Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
D
Submit the data for training without performing any manual transformations. Use the columns that have time-related information to manually split your data. Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets is from 30 days after your validation set.