DA0-002 Data Concepts and Environments Practice Question
A data analyst notices that a column labeled 'Income' contains values like '$50,000' and '$75,000', but also 'High' and 'Low'. What data concept issue is occurring?
⚠ Common exam trap
CompTIA often tests the distinction between data type categories (quantitative vs. qualitative) versus subtypes (discrete/continuous or nominal/ordinal), so candidates mistakenly pick a subtype option when the core issue is the fundamental type mismatch.
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
✓
Mixing quantitative and qualitative data
The 'Income' column contains both numeric values (e.g., '$50,000', '$75,000') which are quantitative data, and categorical labels ('High', 'Low') which are qualitative data. Mixing these two distinct data types in a single column violates data consistency principles and prevents proper statistical analysis or machine learning processing. This is a classic example of mixing quantitative and qualitative 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.
- ✓
Mixing quantitative and qualitative data
Why this is correct
Income should be quantitative, but text labels are qualitative.
- ✗
Mixing discrete and continuous data
Why it's wrong here
Both numeric and text are present; the numeric could be discrete or continuous, but the issue is mixing types.
- ✗
Mixing nominal and ordinal data
Why it's wrong here
Both 'High'/'Low' are ordinal, but the numeric values are quantitative, not categorical.
- ✗
Mixing structured and unstructured data
Why it's wrong here
Structured vs unstructured refers to data format, not value types.
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