DA0-002 Data Concepts and Environments Practice Question
A data analyst is working with a dataset containing customer information. The dataset includes a column 'full_name' which stores first and last names together. To perform analysis on first names separately, which data concept describes the process of splitting 'full_name' into 'first_name' and 'last_name'?
⚠ Common exam trap
Many candidates confuse data normalization with data aggregation or summarization, because both involve restructuring data, but normalization focuses on reducing redundancy and achieving atomicity, not on computing summary statistics.
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 normalization
Data normalization is the process of organizing data to reduce redundancy and improve integrity, which includes splitting composite attributes like 'full_name' into atomic values ('first_name', 'last_name'). This aligns with the first normal form (1NF) principle in database design, where each column should contain indivisible values. The data analyst is decomposing a single field into multiple, more granular fields to enable separate analysis.
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 deduplication
Why it's wrong here
Deduplication removes duplicate records.
- ✗
Data summarization
Why it's wrong here
Summarization creates summaries, not splits.
- ✓
Data normalization
Why this is correct
Normalization reduces redundancy and breaks down attributes.
- ✗
Data aggregation
Why it's wrong here
Aggregation combines data, not splits.
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