hardMultiple Choice
MLA-C01 Practice Question: A data engineer is using Amazon SageMaker Data…
A data engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow for a dataset with 500 columns, many of which are highly correlated. The goal is to reduce dimensionality while preserving interpretability. Which built-in transform in Data Wrangler should be applied?
⚠ Common exam trap
It's easy for candidates to confuse dimensionality reduction with PCA, assuming it is always the best choice, but the question explicitly requires preserving interpretability, which PCA inherently sacrifices.
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
✓
Feature Selection (correlation-based)
The goal is to reduce dimensionality while preserving interpretability. SageMaker Data Wrangler's built-in Feature Selection (correlation-based) transform identifies and removes highly correlated columns, directly reducing the number of features without transforming the original variables into new, uninterpretable components. This preserves the meaning of each selected column, which is essential when interpretability is a priority.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Imputation
Why it's wrong here
Imputation fills missing values; it neither merges correlated columns nor reduces the 500-column count. It is tempting because Data Wrangler offers it for cleaning, but that suits datasets with nulls, not dimensionality reduction for interpretability.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA produces orthogonal principal components that are linear combinations of the original 500 features, destroying the direct feature meaning the goal requires. It is tempting because PCA reduces dimensionality, but that suits scenarios where interpretability is not required.
- ✗
StandardScaler
Why it's wrong here
StandardScaler rescales each feature to zero mean and unit variance, leaving all 500 columns in place. It is tempting because correlated features often need scaling, but that suits distance-based algorithms, not removing correlated columns while preserving interpretability.
- ✓
Feature Selection (correlation-based)
Why this is correct
Correlation-based feature selection drops one of each pair of highly correlated columns, directly reducing dimensionality while retaining the original, interpretable features. Unlike PCA, which produces opaque linear combinations, it satisfies the stem's interpretability constraint and scales to 500 columns without manual inspection.
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