Principal Component Analysis (PCA) is the correct technique because it is an unsupervised linear dimensionality reduction method that preserves variance by projecting data onto orthogonal components ranked by the amount of variance they capture. By selecting only the top principal components, you retain the maximum possible variance while reducing the feature count from 100 to a smaller, more manageable set. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of when to apply PCA versus feature selection methods like filter or wrapper techniques, which do not maximize variance preservation. A common trap is confusing PCA with feature elimination—remember that PCA creates new composite features rather than simply dropping existing ones. For the exam, keep this memory tip: PCA Prioritizes Captured Amount of variance, meaning it orders components by how much variance they explain, so you always choose the top ones to preserve the most information.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 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?
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.
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.
✓
PCA (Principal Component Analysis)
Why this is correct
PCA finds the directions of maximum variance and projects data onto them, preserving as much variance as possible.
Related concept
Read the scenario before looking for a memorised answer.
✗
LDA (Linear Discriminant Analysis)
Why it's wrong here
LDA is a supervised method that maximizes class separability, not variance.
✗
Autoencoders
Why it's wrong here
Autoencoders can learn non-linear transformations and preserve variance, but they are more complex and less interpretable than PCA.
✗
t-SNE
Why it's wrong here
t-SNE is used for visualization of high-dimensional data, not for preserving variance in a lower-dimensional representation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
PCA works by computing the eigenvectors of the covariance matrix of the data; these eigenvectors (principal components) are orthogonal and ordered by their corresponding eigenvalues, which represent the amount of variance explained. A subtle behavior is that PCA assumes linear relationships and is sensitive to feature scaling, so standardizing the data (e.g., using Z-scores) is critical before applying PCA. In a real-world scenario with 100 features, PCA can reduce the feature space to, say, 10 components that capture 95% of the variance, enabling faster model training and reducing overfitting.
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 DA0-001 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DA0-001 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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Question Discussion
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