DA0-002 Data Analysis Practice Question
A data analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)
⚠ Common exam trap
Many candidates confuse data cleaning with data transformation or feature engineering, leading them to select normalization or feature engineering as cleaning steps, when in fact cleaning strictly addresses data quality issues like consistency, completeness, and uniqueness.
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
✓
Correcting inconsistent data
Correcting inconsistent data (Option A) is a core data cleaning step because it ensures that values follow a consistent format, such as standardizing date formats (e.g., 'MM/DD/YYYY' vs 'DD-MM-YYYY') or fixing capitalization (e.g., 'USA' vs 'usa'). This process directly addresses data quality issues that arise from human entry errors or system differences, making the dataset reliable for 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.
- ✓
Correcting inconsistent data
Why this is correct
Standardizing formats and fixing typos are cleaning tasks.
- ✗
Normalization
Why it's wrong here
Normalization scales data, part of transformation, not cleaning.
- ✓
Handling missing values
Why this is correct
Missing values need to be imputed or removed to avoid bias.
- ✗
Feature engineering
Why it's wrong here
Feature engineering creates new variables, part of preprocessing, not cleaning.
- ✓
Removing duplicate records
Why this is correct
Duplicates can skew analysis and must be addressed.
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Written by Johnson Ajibi, MSc IT Security
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