- A
LDA
Why wrong: LDA is a supervised technique for classification and requires labels.
- B
t-SNE
Why wrong: t-SNE is used for visualization and does not produce a transformation for new data.
- C
Autoencoder
Why wrong: Autoencoder is a neural network that can reduce dimensions but is more complex and not the standard choice for simple regression.
- D
PCA
Correct: PCA is widely used for dimensionality reduction in regression tasks.
Question 860 of 1,000
Dimensionality Reduction with PCA — Feature Reduction | CompTIA AI+ Explained
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist wants to reduce the dimensionality of a dataset with 200 features before training a regression model. Which technique should they use?
Quick Answer
The answer is PCA, or Principal Component Analysis, because it is the standard linear dimensionality reduction technique for feature reduction when preparing data for a regression model. PCA works by transforming the original 200 features into a smaller set of uncorrelated principal components that capture the maximum variance in the data, effectively reducing dimensionality while preserving the information most relevant for prediction. On the CompTIA AI+ AI0-001 exam, this question tests your ability to match the right technique to the task: PCA for general feature reduction, t-SNE strictly for visualization, LDA for classification tasks, and autoencoders for complex neural network-based reduction. A common trap is choosing t-SNE because it sounds similar, but remember it does not produce a model you can apply to new data. Memory tip: PCA is for "Pre-Compression of Attributes" — it compresses many features into a few powerful ones for any predictive model.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 a supervised technique for classification and requires labels.
- ✗
t-SNE
Why it's wrong here
t-SNE is used for visualization and does not produce a transformation for new data.
- ✗
Autoencoder
Why it's wrong here
Autoencoder is a neural network that can reduce dimensions but is more complex and not the standard choice for simple regression.
- ✓
PCA
Why this is correct
Correct: PCA is widely used for dimensionality reduction in regression tasks.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
PCA works by computing the covariance matrix of the data, then performing eigenvalue decomposition to extract eigenvectors (principal components) sorted by their corresponding eigenvalues. In practice, the number of components can be chosen by retaining, for example, 95% of the variance, which often reduces 200 features to a much smaller set without losing predictive power. A real-world scenario is in finance, where PCA reduces hundreds of correlated stock return features to a few factors before running a linear regression to predict portfolio risk.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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