Quick Answer
The answer is to investigate the cause of nulls and then apply median imputation. Investigating the root cause is critical because nulls after a merge often stem from mismatched keys or incomplete source data, and blindly filling them can introduce bias. Median imputation is the correct technique for numerical columns because it is robust to outliers and skewed distributions, preserving the central tendency without distorting the dataset’s statistical integrity. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of data quality and preparation, specifically how to handle missing values without losing data or compromising model accuracy. A common trap is jumping straight to deletion or mean imputation, which can skew results; the exam rewards methodical troubleshooting before applying a fix. Remember the mnemonic “Investigate, then Impute” to ensure you always check the cause of nulls after a merge before choosing a robust strategy like the median.
This DA0-001 practice question tests your understanding of mining and acquiring data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
Ignore nulls and proceed.
Why wrong: Ignoring may lead to inaccurate analysis.
Impute nulls with the median.
Median imputation preserves dataset size and reduces bias.
Remove all rows with nulls.
Why wrong: Data loss and bias risk.
Replace nulls with a placeholder value like 'Unknown'.
Why wrong: Placeholders may not be suitable for numeric columns.
Investigate the cause of nulls.
Understanding why nulls occurred helps improve data quality.
Answer choices
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 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may 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.
Detailed technical explanation
Imputation with the median works by calculating the median of the non-null values in a column and substituting that value for each null. This method assumes the data is missing completely at random (MCAR) or at least not dependent on the missing value itself. In practice, analysts often use the median over the mean when the data contains outliers, as the median is unaffected by extreme values, ensuring the imputed values do not skew the distribution.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Mining and Acquiring Data — This question tests Mining and Acquiring Data — Read the scenario before looking for a memorised answer..
The correct answer is: Impute nulls with the median. — Option B is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-001 exam.
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