This AI0-001 practice question tests your understanding of ai models and data engineering. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?
Principal component analysis (PCA)
Why wrong: PCA reduces dimensionality and is not applied to the target variable.
Standardization (Z-score)
Why wrong: Standardization centers and scales data but does not reduce skewness.
One-hot encoding
Why wrong: One-hot encoding is for categorical variables, not for transforming a continuous target.
Log transformation
Log transformation reduces right skewness by compressing large values.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Log transformation
Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.
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.
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality and is not applied to the target variable.
Standardization (Z-score)
Why it's wrong here
Standardization centers and scales data but does not reduce skewness.
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not for transforming a continuous target.
Log transformation
Why this is correct
Log transformation reduces right skewness by compressing large values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
CompTIA often tests the misconception that standardization can fix skewness, but candidates must remember that standardization only rescales the data, not reshape its distribution.
Detailed technical explanation
Under the hood, log transformation applies the natural logarithm (or base-10) to each value, which is a monotonic transformation that preserves order while reducing the impact of large outliers. In real-world scenarios like house price prediction, where prices often follow a log-normal distribution, applying log transformation can make the relationship between features and target more linear, improving model accuracy and interpretability. A subtle behavior is that log(0) is undefined, so a small constant (e.g., 1) must be added if the target contains zero values.
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 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
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Log transformation — Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.
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.
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Last reviewed: Jun 30, 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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