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MLA-C01 Data Preparation for Machine Learning Practice Question

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

⚠ Common exam trap

AWS often tests the misconception that standard preprocessing techniques like imputation or scaling can be applied globally to the entire dataset, when in time-series contexts they must be computed only from the training set to avoid leakage.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Standardize features using parameters computed only from the training set

Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Impute missing values using the mean of the entire dataset

    Why it's wrong here

    Using the full dataset mean leaks information from test data.

  • Standardize features using parameters computed only from the training set

    Why this is correct

    Computing mean and variance only on training data prevents leakage from test.

  • Use a time-based train/test split

    Why this is correct

    Ensures training data is chronologically before test data.

  • Use only past data for feature engineering (e.g., lag features)

    Why this is correct

    Lag features based on past observations do not leak future information.

  • Shuffle the data randomly before splitting

    Why it's wrong here

    Shuffling ignores time order, causing future data to influence training.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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