MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. Which TWO features of Data Wrangler can be used to handle imbalanced classification problems? (Choose two.)

⚠ Common exam trap

Candidates often confuse data preprocessing techniques (like scaling or encoding) with class imbalance handling methods, leading them to select Standardization or one-hot encoding as solutions for imbalanced data.

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

✓

The Random Oversampling transform to duplicate minority class instances.

Amazon SageMaker Data Wrangler includes a built-in Random Oversampling transform that duplicates instances of the minority class to balance the class distribution. This directly addresses imbalanced classification by increasing the representation of the underrepresented class without generating synthetic data.

Answer analysis

Option-by-option breakdown

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

✓
The Random Oversampling transform to duplicate minority class instances.
Why this is correct
Oversampling increases the minority class size.
✓
The SMOTE transform to generate synthetic samples for the minority class.
Why this is correct
SMOTE creates synthetic minority samples to balance classes.
✗
The Drop Duplicates transform to remove redundant rows.
Why it's wrong here
Removing duplicates does not target imbalance.
✗
Standardization to scale numerical features.
Why it's wrong here
Scaling does not affect class distribution.
✗
One-hot encoding for categorical variables.
Why it's wrong here
One-hot encoding changes feature representation but not class balance.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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