DA0-002 Data Concepts and Environments Practice Question
A data analyst receives a dataset with a column 'salary' that contains values like '45,000', '55,000', and '65,000'. The analyst notices that the values are stored as text. Which data concept should be applied to convert the salary column from text to numeric format for analysis?
⚠ Common exam trap
CompTIA often tests the distinction between data transformation (type conversion) and data preparation techniques like imputation or normalization, trapping candidates who confuse 'changing format' with 'filling gaps' or 'scaling values'.
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
✓
Data type conversion
Data type conversion is the correct concept because the salary values are stored as text (string) but need to be converted to a numeric type (e.g., integer or float) for mathematical operations like aggregation or averaging. In tools like Python (pandas `astype(float)`), SQL (`CAST(salary AS INTEGER)`), or Excel (`VALUE()` function), this explicit conversion ensures the data is treated as numbers, not strings. Without conversion, operations like `SUM` or `AVG` would fail or produce incorrect results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data imputation
Why it's wrong here
Imputation fills missing values with substituted estimates such as the mean or median; it never changes a column's data type. The salary values are present but stored as text, so parsing and type conversion are required. Imputation would be correct where rows contain nulls rather than formatted strings.
- ✓
Data type conversion
Why this is correct
Data type conversion casts the text values to a numeric type, stripping the comma separators so arithmetic and aggregation work correctly. The salary column's text storage is the constraint, and conversion changes its type rather than its values.
- ✗
Data validation
Why it's wrong here
Validation checks whether values meet rules such as range or format, flagging non-conforming records; it does not alter storage type. The salary strings are valid text, so validation passes them unchanged. Validation would be correct when enforcing constraints like a minimum salary threshold.
- ✗
Data normalization
Why it's wrong here
Normalization rescales numeric values into a common range, such as 0 to 1, and assumes the data is already numeric. Here the salary column is text containing thousands separators, so the values must first be parsed and cast to a numeric type. Normalization suits magnitude differences, not type conversion.
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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.