- A
One-hot encoding
Why wrong: One-hot encoding increases the number of features by creating binary columns for each category.
- B
Imputation
Why wrong: Imputation fills missing values but does not affect the number of features.
- C
Feature scaling
Why wrong: Feature scaling normalizes feature ranges without changing the number of features.
- D
Principal Component Analysis (PCA)
PCA reduces dimensionality by projecting data onto principal components.
- E
Feature selection
Feature selection reduces the number of features by selecting a subset of the original variables.
Question 389 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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.
Which TWO data preprocessing techniques reduce the dimensionality of a dataset?
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
✓
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated variables (principal components) ordered by the variance they capture. By selecting only the top components, PCA reduces the number of features while retaining most of the dataset's information, effectively lowering the dimensionality.
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.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding increases the number of features by creating binary columns for each category.
- ✗
Imputation
Why it's wrong here
Imputation fills missing values but does not affect the number of features.
- ✗
Feature scaling
Why it's wrong here
Feature scaling normalizes feature ranges without changing the number of features.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensionality by projecting data onto principal components.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Feature selection
Why this is correct
Feature selection reduces the number of features by selecting a subset of the original variables.
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 techniques that transform or select features (reducing dimensionality) versus those that prepare data for modeling (like encoding, imputation, or scaling) without changing the number of features.
Detailed technical explanation
How to think about this question
PCA works by computing the eigenvectors and eigenvalues of the covariance matrix of the data, where eigenvectors represent the directions of maximum variance and eigenvalues indicate the magnitude of variance along those directions. In practice, PCA is sensitive to feature scaling, so it is common to standardize features before applying PCA to ensure all features contribute equally. A real-world scenario is image compression, where PCA can reduce the number of pixel features while preserving the essential structure of the image.
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?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Principal Component Analysis (PCA) — Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated variables (principal components) ordered by the variance they capture. By selecting only the top components, PCA reduces the number of features while retaining most of the dataset's information, effectively lowering the dimensionality.
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
This AI0-001 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 AI0-001 exam.
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