Exam Professional Machine Learning Engineer topic 1 question 18 discussion

Should be D. As time signal that is spread across multiple columns so manual split is required.

C You use the Time column to tell AutoML Tables that time matters for your data; it is not randomly distributed over time. When you specify the Time column, AutoML Tables use the earliest 80% of the rows for training, the next 10% of rows for validation, and the latest 10% of rows for testing. AutoML Tables treats each row as an independent and identically distributed training example; setting the Time column does not change this. The Time column is used only to split the data set. You must include a value for the Time column for every row in your dataset. Make sure that the Time column has enough distinct values, so that the evaluation and test sets are non-empty. Usually, having at least 20 distinct values should be sufficient. https://cloud.google.com/automl-tables/docs/prepare#time

D could work, but I'm still leaning towards C

AutoML handles training, validation, test splits automatically for you when you specify a Time column. There is no requirement to do this manually.

D)D is correct, as this would satisfy the days criteria mentioned in the question. 30 days is more than 20 days, and the prediction model can be used on a validation dataset to validate the results for the next 20 days.

thinking that "spread across multiple columns" seems like "columns with redundant information," and considering how AutoML can deal with correlated columns, I think option C is the best choice, with no need for a manual split. However, "time information is not contained in a single column" is the same thing as "time signal that is spread across multiple columns." I agree that D could be the best option. Then, I tend to think that D is the best choice because the text could be more clearly expressed in redundant options.

Either C or D but leaning towards C as not get the 30 days in D

"data has a time signal that is spread across multiple columns" - I interpret as having > 1 timeseries column. AutoML knows how to deal with a single column but not multiple hence answer is D

Since AutoML is good enough to perform the splits, C appears to be the right answer. Moreover, time information across multiple columns which requires manual split as per option D is different from the question's scenario where the time signal is spread across multiple columns which can be hours, months, days, etc. if we can define in AutoML the right time signal column, its enojugh to split the data and pick most recent data as test data and earliest data as test data

A Wrong, Even if columns are combines into a 1D-array(column), the time signal should be noticed to autoML anyway. Automatic split cannot work since we need more than 20 days history B Wrong, Without indicating time signal to AutoML, data would leak in (time leakage) in training/validation/test sets C Wrong, but might be possible if time signal wouldn't have bee spread across multiple columns D True, because time signal is spread accross multiple columns require to manually split the data. Since we want to predict LTV over the next 20 days, it is necessary to have at least 20 days history between the splits (30 seems okay: 10 days predictions) Validating and testing on the last 2 months seems reasonable for marketing purpose (usually seasonal).

Why 30 days after each data sets, even though we need to predict only for 20 days?

Agree with kkd14. D should be the correct answer.

As far as I understand, that AutoML table can handle time-signal column full automatically. Thus, I went to C.

Went with D

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. This approach ensures that AutoML can handle the time-based nature of the data properly. By providing the Time column, AutoML can automatically split the data in a way that respects the time-based structure, using more recent data for validation and testing. This approach is especially important for time-series data, as it helps prevent leakage of future information into the training set, ensuring a more accurate and reliable model.

https://cloud.google.com/automl-tables/docs/data-best-practices#time - If the time information is not contained in a single column, you can use a manual data split to use the most recent data as the test data, and the earliest data as the training data.

I go with D: https://cloud.google.com/automl-tables/docs/data-best-practices#time Read it carefully at the last paragraph of the topic: If the time information is not contained in a single column, you can use a manual data split to use the most recent data as the test data, and the earliest data as the training data.