Quick Answer
The answer is to apply standard scaling (Z-score normalization) because it transforms the 'Income' column by removing the mean and scaling to unit variance, which effectively handles the mixed scales (thousands and hundreds) while remaining robust to outliers. In contrast, min-max scaling squeezes values into a fixed range like 0 to 1, but it is highly sensitive to extreme values, making it a poor choice when outliers are present. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of feature scaling for machine learning, specifically the trade-off between standard scaling vs min-max. A common trap is choosing min-max because it seems simpler, but the exam emphasizes that standard scaling is the safer default when data has varying scales and potential outliers. Remember the memory tip: "Z-score for zero-centered, robust to outliers; min-max for bounded, but breaks with extremes."
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 dataset contains a column 'Income' with values in different scales (some in thousands, some in hundreds). What is the best way to standardize this column for use in a machine learning model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Apply min-max scaling to range [0,1]
Why wrong: Min-max scaling is sensitive to outliers and doesn't center data.
Apply standard scaling (Z-score normalization)
Standard scaling centers and scales data, suitable for inconsistent scales.
Apply log transformation
Why wrong: Log transformation reduces skew but does not standardize scale.
Remove the column
Why wrong: Removing data is not best practice if transformation is possible.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Apply standard scaling (Z-score normalization)
Standard scaling (Z-score) removes the mean and scales to unit variance, making values comparable. Min-max scaling also works but doesn't handle outliers well. Option A is correct.
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.
Apply min-max scaling to range [0,1]
Why it's wrong here
Min-max scaling is sensitive to outliers and doesn't center data.
Apply standard scaling (Z-score normalization)
Why this is correct
Standard scaling centers and scales data, suitable for inconsistent scales.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Apply log transformation
Why it's wrong here
Log transformation reduces skew but does not standardize scale.
Remove the column
Why it's wrong here
Removing data is not best practice if transformation is possible.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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 DA0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Analyzing and Modeling Data — study guide chapter
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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: Apply standard scaling (Z-score normalization) — Standard scaling (Z-score) removes the mean and scales to unit variance, making values comparable. Min-max scaling also works but doesn't handle outliers well. Option A is correct.
Identify which DA0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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.
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