DA0-002 Data Analysis Practice Question
A data scientist is working with a dataset containing 1000 features and 500 samples. The goal is to build a predictive model. Which technique should be used to reduce the number of features while retaining most of the variance?
⚠ Common exam trap
CompTIA often tests the distinction between supervised feature selection (Lasso, Forward selection) and unsupervised dimensionality reduction (PCA), trapping candidates who confuse regularization with variance-based reduction.
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
✓
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original features into a set of orthogonal components, ordered by the variance they capture. Given 1000 features and only 500 samples, PCA is ideal because it reduces the feature space while retaining the maximum variance, helping to avoid overfitting and the curse of dimensionality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ridge regression
Why it's wrong here
Ridge regression shrinks coefficients via L2 regularisation but retains all 1000 features, so it never reduces dimensionality. It tempts because it handles high-dimensional data and multicollinearity well, and would be correct when the goal is stable coefficient estimation rather than feature elimination.
- ✗
Forward selection
Why it's wrong here
Forward selection adds features greedily to optimise model performance, but it does not explicitly retain variance and is computationally costly across 1000 features. It tempts as a wrapper-based feature selection method, and would suit scenarios prioritising predictive accuracy over variance preservation.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA projects the 1000 features onto orthogonal principal components ordered by explained variance, letting you keep the top components that retain most variance while discarding the rest, reducing dimensionality despite having fewer samples than features.
- ✗
Lasso regression
Why it's wrong here
Lasso performs L1 regularisation, shrinking some coefficients to zero and thus selecting features, but it targets predictive performance rather than maximising retained variance. It tempts because it genuinely reduces feature count, and would be correct when seeking a sparse model for prediction rather than variance retention.
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