DA0-002 Data Analysis Practice Question
A data analyst is working with a dataset containing a categorical variable 'Education Level' with categories: High School, Bachelor's, Master's, PhD. The analyst wants to include this variable in a regression model. Which encoding technique should the analyst use?
⚠ Common exam trap
The trap here is using label encoding for a nominal variable, which forces the model to treat categories as ordered numbers and can lead to incorrect coefficient estimates.
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
✓
One-hot encoding
One-hot encoding is the preferred method for nominal categorical variables in regression because it avoids imposing an artificial order or equal spacing. It creates separate binary indicators, allowing the model to estimate distinct effects for each education level. Label, binary, and ordinal encoding all introduce numerical relationships that may misrepresent the categorical nature of the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Binary encoding
Why it's wrong here
Binary encoding converts categories into binary code, reducing dimensionality but still imposing a numerical structure that may not be meaningful. It is often used for high-cardinality features, but for a small set of education levels, one-hot encoding is more interpretable and standard. Binary encoding can complicate interpretation in regression.
- ✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers to categories (e.g., High School=0, Bachelor's=1), which implies an ordinal relationship and equal spacing. For nominal data like Education Level, this can mislead the regression model into treating the categories as ordered. It is suitable for ordinal variables but not for this nominal variable.
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Ordinal encoding
Why it's wrong here
Ordinal encoding assigns ordered integers based on category rank, which is appropriate for ordinal variables like satisfaction levels. Education Level could be considered ordinal, but the analyst may not want to assume equal intervals between degrees. One-hot encoding is safer when the spacing between categories is not uniform or when the analyst wants to avoid imposing order.
- ✓
One-hot encoding
Why this is correct
One-hot encoding creates binary columns for each category, avoiding implying any ordinal relationship. For regression, it is appropriate for nominal categorical variables like Education Level, which has no inherent order. It allows the model to estimate a separate coefficient for each category, capturing differences without assuming a linear progression.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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