DA0-002 Data Analysis Practice Question
A company has a dataset with 100 features. The data analyst wants to reduce dimensionality while preserving as much variance as possible. Which technique should be used?
⚠ Common exam trap
Many candidates confuse PCA with LDA because both are linear transformations, but LDA requires labeled data and maximizes class separation, not variance, making it unsuitable for this unsupervised variance-preservation goal.
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 (Principal Component Analysis)
PCA is the correct choice because it is an unsupervised linear dimensionality reduction technique that projects the data onto orthogonal components ordered by the variance they capture. By selecting the top principal components, the analyst can retain the maximum possible variance in the dataset while reducing the number of features from 100 to a smaller set, directly addressing the goal of preserving variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
PCA (Principal Component Analysis)
Why this is correct
Principal Component Analysis projects the 100 features onto orthogonal components ordered by explained variance, so retaining the leading components captures maximum variance in fewer dimensions. This directly satisfies the goal of reducing dimensionality while preserving as much variance as possible.
- ✗
LDA (Linear Discriminant Analysis)
Why it's wrong here
LDA is a supervised method that maximizes class separability, not variance.
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Autoencoders
Why it's wrong here
Autoencoders are a neural-network-based approach for non-linear dimensionality reduction, but they require large datasets and extensive hyperparameter tuning to avoid overfitting, making them unreliable for preserving maximum variance in a 100-feature dataset without a substantial number of samples. This technique is tempting because it can learn complex, non-linear latent representations, and would be correct when the goal is to capture intricate patterns in high-dimensional data with abundant training examples, such as image or signal compression.
- ✗
t-SNE
Why it's wrong here
t-SNE is a non-linear technique for visualising high-dimensional data in two or three dimensions; it preserves local neighbourhood structure rather than global variance and is unsuitable for feeding downstream models. It would be correct for exploratory cluster visualisation, not variance-preserving reduction.
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