MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)
⚠ Common exam trap
AWS often tests the distinction between data preparation steps that are universally applicable (like imputation and standardization) versus those that are task-specific or harmful (like blind outlier removal or using complex models for preprocessing), tempting candidates to choose options that seem plausible but are technically incorrect.
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 numeric features
Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize text data to lowercase
Why it's wrong here
Although sometimes useful, it is a specific step and not a general necessary part of data preparation for all datasets.
- ✗
Remove all outliers blindly
Why it's wrong here
Blind removal can discard valuable data and bias the model.
- ✓
Standardize numeric features
Why this is correct
Standardization (e.g., z-score) helps many algorithms converge faster.
- ✗
Use a large neural network to handle all transformations
Why it's wrong here
Neural networks are models, not data preparation techniques.
- ✓
Impute missing values using mean or median
Why this is correct
Imputation preserves data size and is a common practice.
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