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.
Option B is correct because imputing nulls with the median is a robust statistical technique that fills missing numeric values with the column's central tendency, reducing data loss while limiting the influence of outliers compared to the mean. Option E is correct because investigating the cause of nulls is an essential diagnostic step: nulls introduced by a dataset merge often indicate unmatched join keys, schema mismatches, or missing source records, and understanding the root cause determines whether imputation, key correction, or source remediation is appropriate. Options A, C, and D are not among the correct answers: ignoring nulls (A) can bias analysis and break models, removing all rows with nulls (C) can discard substantial valid data and skew distributions, and replacing nulls with a placeholder like 'Unknown' (D) is only suitable for categorical data and can corrupt numeric columns or mislead downstream processing.
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
Proceeding without addressing the nulls leaves downstream aggregates, joins and models silently wrong, since many algorithms and SQL operations propagate or exclude nulls unpredictably. It is tempting when nulls are few and harmless, but the stem states many columns are affected, so the merge keys or source data must be investigated and remediated.
- ✓
Impute nulls with the median.
Why this is correct
Median imputation fills missing numeric values with the column's central value, which is robust to outliers and preserves the row count needed for analysis. It satisfies the requirement to handle nulls introduced by the merge without discarding records, though it reduces variance and should follow cause investigation.
- ✗
Remove all rows with nulls.
Why it's wrong here
Dropping every row containing a null discards valid data in unaffected columns, shrinking the dataset and biasing results. It is tempting as a quick cleanse, and is defensible only when nulls are rare and random, but here nulls are widespread after the merge, so imputation or source reconciliation is required instead.
- ✗
Replace nulls with a placeholder value like 'Unknown'.
Why it's wrong here
Substituting a literal 'Unknown' string converts a null into a valid-looking category, corrupting numeric columns and skewing aggregates and model training. It is tempting because it preserves row counts, and would suit genuinely categorical fields where a missing category is meaningful, not widespread post-merge nulls needing diagnosis.
- ✓
Investigate the cause of nulls.
Why this is correct
Nulls appearing after a merge usually signal mismatched join keys, schema differences or missing source records. Establishing the cause determines whether imputation, key correction or exclusion is appropriate, preventing the analyst from masking a structural join defect with fabricated values.
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