AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist wants to reduce the dimensionality of a dataset with 200 features before training a regression model. Which technique should they use?
⚠ Common exam trap
CompTIA often tests the distinction between supervised and unsupervised techniques, and the trap here is that candidates confuse LDA (supervised, classification) with PCA (unsupervised, regression-friendly) because both are linear methods for dimensionality 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
✓
PCA
PCA (Principal Component Analysis) is the correct technique because it is an unsupervised linear dimensionality reduction method that identifies the directions (principal components) of maximum variance in the data. For a dataset with 200 features, PCA can reduce dimensionality while preserving as much variance as possible, which is ideal before training a regression model to avoid overfitting and multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
LDA
Why it's wrong here
LDA is supervised and maximises class separability, so it needs labelled categorical targets; this regression task has a continuous outcome and no classes to separate. It is tempting because LDA genuinely reduces dimensionality, but only for classification, where it would be the right pick.
- ✗
t-SNE
Why it's wrong here
t-SNE is a non-linear visualisation method that preserves local neighbourhoods for two- or three-dimensional plots; it does not produce a reusable transform for new regression data. It is tempting for exploring clusters, but PCA or similar linear reduction is required when feeding a model.
- ✗
Autoencoder
Why it's wrong here
An autoencoder reduces dimensionality by learning a non-linear compressed representation through an encoding-decoding bottleneck, but it requires large amounts of data to train effectively and introduces a black-box latent space that is difficult to interpret for regression. It is tempting because autoencoders excel at unsupervised feature learning for reconstruction tasks, such as denoising images or anomaly detection, where preserving input structure matters more than linear interpretability.
- ✓
PCA
Why this is correct
Principal component analysis projects the 200 correlated features onto a smaller set of orthogonal components that retain most variance, reducing dimensionality before regression. It is the standard unsupervised linear technique for this purpose, unlike feature-selection methods that simply drop columns.
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