AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist is preparing a dataset for a binary classification neural network. The dataset contains both numerical and categorical features, and some rows have identical entries. Which TWO preprocessing steps are most essential to improve model performance and avoid overfitting?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between preprocessing steps (applied to raw data) and model-level regularization techniques (like dropout), tricking candidates into selecting dropout as a preprocessing step when it is actually part of the model architecture.
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
✓
Removing duplicate records
Removing duplicate records (A) is essential because identical rows can artificially inflate the importance of certain patterns, leading the model to memorize noise rather than generalize. This directly reduces overfitting by ensuring the training set reflects true data distribution. Scaling numerical features (B) to zero mean and unit variance (standardization) is critical for neural networks as it prevents features with larger magnitudes from dominating gradient updates, enabling faster convergence and stable training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Removing duplicate records
Why this is correct
Duplicate records can cause the model to overfit to repeated patterns.
- ✓
Scaling numerical features to have zero mean and unit variance
Why this is correct
Scaling ensures that features contribute equally to the loss, improving convergence.
- ✗
Increasing the batch size
Why it's wrong here
Batch size is a hyperparameter, not a preprocessing step.
- ✗
Applying PCA for dimensionality reduction
Why it's wrong here
PCA is not always essential and may lose information; it is a feature reduction technique, not a standard preprocessing step for all models.
- ✗
Using dropout regularization in the model
Why it's wrong here
Dropout is a regularization technique applied during training, not a preprocessing step.
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