Quick Answer
The correct answer is Principal Component Analysis (PCA). PCA is the ideal technique for dimensionality reduction when you have a dataset with 1000 features and only 500 samples, because it is an unsupervised method that transforms the original features into a smaller set of orthogonal components, each capturing the maximum possible variance from the data. This directly addresses the curse of dimensionality and helps prevent overfitting by reducing the feature space while retaining the most informative structure. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of when to apply PCA versus feature selection methods like filter or wrapper techniques—a common trap is choosing a supervised method like LDA, which requires labeled data. Remember the memory tip: "PCA prioritizes variance, not labels," so when the goal is to compress many features without using target variables, PCA is your go-to tool.
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 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?
Ridge regression
Why wrong: Ridge does not reduce features; it shrinks coefficients.
Forward selection
Why wrong: Forward selection is a feature selection method but may not capture variance optimally.
Principal Component Analysis (PCA)
PCA reduces dimensionality by creating new features that capture maximum variance.
Lasso regression
Why wrong: Lasso performs feature selection but is supervised and may not retain variance.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 does not reduce features; it shrinks coefficients.
Forward selection
Why it's wrong here
Forward selection is a feature selection method but may not capture variance optimally.
Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensionality by creating new features that capture maximum variance.
Related concept
Read the scenario before looking for a memorised answer.
Lasso regression
Why it's wrong here
Lasso performs feature selection but is supervised and may not retain variance.
Common exam traps
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.
Detailed technical explanation
PCA works by computing the eigenvectors of the covariance matrix of the data; the top k eigenvectors (principal components) capture the directions of maximum variance. In high-dimensional settings like 1000 features vs 500 samples, PCA can be efficiently computed using singular value decomposition (SVD) without explicitly forming the covariance matrix, which is computationally expensive. A real-world scenario is in genomics, where PCA reduces thousands of gene expression features to a few components for clustering or visualization.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — study guide chapter
Learn the concepts, then practise the questions
Analyzing and Modeling Data practice questions
Targeted practice on this topic area only
All DA0-001 questions
509 questions across all exam domains
CompTIA Data+ DA0-001 study guide
Full concept coverage aligned to exam objectives
DA0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise DA0-001 questions linked to Comparing and Contrasting Data Concepts.
Practise DA0-001 questions linked to Mining and Acquiring Data.
Practise DA0-001 questions linked to Analyzing and Modeling Data.
Practise DA0-001 questions linked to Visualizing Data.
Practise DA0-001 questions linked to Communicating Data Insights.
Practise DA0-001 questions linked to CompTIA A+ hardware.
Practise DA0-001 questions linked to CompTIA A+ mobile devices.
Practise DA0-001 questions linked to CompTIA A+ networking.
Practise DA0-001 questions linked to CompTIA A+ operating systems.
Practise DA0-001 questions linked to CompTIA A+ security.
Practise DA0-001 questions linked to CompTIA A+ software troubleshooting questions.
Practise DA0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 30, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-001 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.