DA0-002 Data Acquisition and Preparation Practice Question
After merging two datasets, an analyst finds that the resulting dataset has many null values in some columns. Which TWO steps should the analyst take to address this? (Select two.)
⚠ Common exam trap
Watch out — candidates often think 'Ignore nulls and proceed' is acceptable, but the exam tests the understanding that nulls must be actively handled to ensure data quality and model validity, not simply overlooked.
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
✓
Impute nulls with the median.
Imputing nulls with the median is a standard technique for handling missing numerical data, especially when the distribution is skewed or contains outliers. The median is robust to extreme values and preserves the central tendency of the column, making it a safe choice for many analytical models. This approach avoids data loss while maintaining statistical integrity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore nulls and proceed.
Why it's wrong here
Ignoring may lead to inaccurate analysis.
- ✓
Impute nulls with the median.
Why this is correct
Median imputation preserves dataset size and reduces bias.
- ✗
Remove all rows with nulls.
Why it's wrong here
Data loss and bias risk.
- ✗
Replace nulls with a placeholder value like 'Unknown'.
Why it's wrong here
Placeholders may not be suitable for numeric columns.
- ✓
Investigate the cause of nulls.
Why this is correct
Understanding why nulls occurred helps improve data quality.
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