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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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The core principle is to use historical data (the training set) to build a model and then evaluate its performance on future, unseen data (the test set). So, the dataset must be split into training and testing sets based on time (temporal split/out-of-time split/chronological split). This approach simulates real-world scenarios where models must make predictions on data that occurred after they were trained, making it especially useful for time series forecasting.
D is incorrect: Applying transformations before splitting is important, but it does not resolve the issue of time leakage. Even if transformations are done correctly, the random split will still lead to inflated test accuracy and poor production performance. This option focuses on correct data processing, but it does not address the leakage caused by random splitting in time series data.
It's D