DA0-002 Data Concepts and Environments Practice Question
Exhibit
{
"report": "Analytics",
"filters": [
{"field": "transaction_date", "operator": ">=", "value": "2023-01-01"},
{"field": "region", "operator": "=", "value": "West"}
],
"metrics": ["revenue", "units_sold"]
}Refer to the exhibit. Which type of data is the field "region"?
⚠ Common exam trap
Watch out — candidates often confuse 'region' with a numeric code (e.g., region ID 1, 2, 3) and incorrectly classify it as discrete quantitative data, but the field 'region' as shown contains text labels, making it qualitative.
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
✓
Qualitative
The field 'region' contains categorical labels (e.g., 'North', 'South', 'East', 'West') that represent distinct groups or categories, not numerical measurements. Qualitative data (also called categorical data) describes attributes or characteristics that can be named but not meaningfully ordered or measured on a numeric scale. Since 'region' assigns a name to a geographic area without any inherent numeric value or order, it is a classic example of 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.
- ✓
Qualitative
Why this is correct
Region labels categories such as "North" or "EMEA", so it is qualitative (nominal) data. It cannot be measured or averaged, unlike quantitative fields. This satisfies the stem's request to classify the field by its data type.
- ✗
Continuous
Why it's wrong here
Region is categorical, not continuous; continuous data takes any value on a numeric scale. It is tempting because continuous is one of the standard data-type classifications, but region values are named labels with no numeric ordering or measurable interval between them.
- ✗
Quantitative
Why it's wrong here
Quantitative data is numeric and measurable; region values are text labels without magnitude. It is tempting because quantitative is a common classification axis, but it describes counts or measurements, whereas region is qualitative categorical data.
- ✗
Discrete
Why it's wrong here
Discrete data counts whole numeric values, whereas region holds named categories such as countries or states. It is tempting because discrete is a recognised data-type category, but it applies to countable quantities, not to qualitative labels like region.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 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 DA0-002 exam.