AIF-C01 Fundamentals of AI and ML Practice Question
A data science team is preparing a dataset for training a machine learning model. They need to perform data preprocessing to improve model performance. Which TWO of the following are common data preprocessing techniques? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse model training techniques like gradient descent or evaluation methods like cross-validation with data preprocessing steps.
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
✓
One-hot encoding
Normalization and one-hot encoding are standard preprocessing techniques. Normalization scales numerical features, while one-hot encoding transforms categorical variables into a numerical format. Both are applied to the data before training to ensure the model can effectively learn from all 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.
- ✓
One-hot encoding
Why this is correct
One-hot encoding converts categorical variables into a binary vector representation, allowing machine learning algorithms to handle non-numeric data. It is a fundamental preprocessing step for categorical features, ensuring they can be used in models that require numerical input. This is a common technique.
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Feature selection
Why it's wrong here
Feature selection is the process of selecting a subset of relevant features for model training. While important, it is typically considered a feature engineering step rather than a core data preprocessing technique like scaling or encoding. It aims to reduce dimensionality and improve model interpretability, but it is not always required.
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Cross-validation
Why it's wrong here
Cross-validation is a model evaluation technique that assesses how well a model generalizes to unseen data. It is not a data preprocessing step; it is used after preprocessing and during model selection. It helps estimate performance but does not transform the data itself.
- ✓
Normalization
Why this is correct
Normalization scales numerical features to a standard range, such as 0 to 1, which helps algorithms that are sensitive to feature scales, like gradient descent. It ensures that all features contribute equally to the model's learning process, improving convergence and performance. This is a standard preprocessing step.
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Gradient descent
Why it's wrong here
Gradient descent is an optimization algorithm used to minimize the loss function during model training. It is not a data preprocessing technique; it operates on the model parameters, not the data. Preprocessing occurs before training, while gradient descent is part of the training process itself.
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