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

The correct answer is C the model will be trained on earlier data and tested on more recent data, which matches how it will be used in production to predict future LTV. This chronological splitting is essential for accurate evaluation of time-series forecasting models.

C is correct answer

D is Correct - A time signal spread across multiple columns in a spreadsheet or data table would typically represent a time-series data where each column corresponds to a specific time point or interval, and the values in each column represent the signal's value at that time

C is the right answer as manually splitting data data based on time adds unnecessary complexity. AutoML Tables can handle the time-based splits for us automatically when we specify the time column. Option D requires more manual intervention and introduces the risk of making errors in the data splitting process.

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.