MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is preparing a dataset for a time series forecasting model. The dataset contains a timestamp column and a target variable. The engineer wants to create additional features such as lag values and rolling averages. Which SageMaker Data Wrangler transform should be used to generate these time series features?
⚠ Common exam trap
The trap here is assuming any transform that manipulates numerical data can create lag features; only the Time Series transform is designed for temporal feature engineering.
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
✓
Use the 'Time Series' transform to create lag and rolling window features.
Time series feature engineering often involves creating lag features (past values) and rolling statistics (e.g., moving averages) to capture temporal patterns. In SageMaker Data Wrangler, the 'Time Series' transform provides a dedicated set of operations for these tasks, including lag, rolling window, and date part extraction. This transform simplifies the process and ensures correct handling of time order. Other transforms like text featurization or outlier handling serve different purposes and cannot produce these features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'Handle Outliers' transform to create lag features.
Why it's wrong here
The 'Handle Outliers' transform is used to detect and treat outliers in numerical data. It does not generate new features such as lags or rolling averages. Therefore, it is not appropriate for creating time series features.
- ✗
Use the 'Balance Data' transform to generate rolling averages.
Why it's wrong here
The 'Balance Data' transform is used to address class imbalance by oversampling or undersampling. It does not perform any time series feature engineering. It is unrelated to creating lag or rolling features, making it incorrect for this scenario.
- ✓
Use the 'Time Series' transform to create lag and rolling window features.
Why this is correct
SageMaker Data Wrangler includes a 'Time Series' transform that can generate lag features, rolling statistics (like mean, sum), and other time-based features from a timestamp column. This transform is specifically designed for time series data preparation, allowing the engineer to create the required lag values and rolling averages efficiently.
- ✗
Use the 'Featurize Text' transform to extract date parts.
Why it's wrong here
The 'Featurize Text' transform is for natural language processing, such as tokenization or vectorization. It does not handle time series feature generation like lags or rolling averages. While you can extract date parts using other transforms, this one is not suitable for the described task.
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