DA0-002 Data Acquisition and Preparation Practice Question
A data analyst at a healthcare clinic is preparing a patient records dataset for analysis. The analyst discovers that the 'date_of_birth' column contains values stored as text strings in the format 'MM/DD/YYYY', but the analytics tool requires a date data type for age calculations. Which data transformation technique should the analyst apply?
⚠ Common exam trap
Many candidates confuse data type conversion with normalization or aggregation, which address different data preparation needs.
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
The dataset stores dates as text, which prevents date arithmetic. Converting the column to a date data type is the correct transformation because it changes the underlying representation to one that supports date functions. This enables accurate age calculations and other temporal operations required by the analytics tool.
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 aggregation
Why it's wrong here
Aggregation combines multiple rows into summary values, such as sums or averages. The problem here is not about summarizing data but about the format of individual values. Aggregation would not change the underlying data type and would not resolve the inability to perform date calculations.
- ✗
Data imputation
Why it's wrong here
Imputation fills in missing values with estimated ones. The date_of_birth column likely has no missing values; the issue is that existing values are stored as text. Imputation would not address the type mismatch and could introduce incorrect values if applied unnecessarily.
- ✗
Data normalization
Why it's wrong here
Normalization typically refers to scaling numeric values to a common range or organizing database tables to reduce redundancy. In this scenario, the issue is not about scaling or redundancy but about the data type mismatch. Applying normalization would not convert text to date and would not enable age calculations.
- ✓
Data type conversion
Why this is correct
Converting the text strings to a date data type allows the analytics tool to perform date arithmetic such as calculating age. This transformation changes the representation of the data to match the required format, enabling correct calculations and comparisons. It is the appropriate step when data is stored in an incompatible type.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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