DA0-002 Data Analysis Practice Question
A data analyst is reviewing a dataset of customer transactions and notices that the 'transaction_amount' column contains several negative values. The analyst suspects these are refunds rather than errors. Which data validation technique should the analyst apply to confirm this?
⚠ Common exam trap
It's easy for candidates to confuse data cleaning with data validation; removing or replacing negative values might seem like a quick fix, but validation requires confirming the values' legitimacy first.
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
✓
Cross-reference negative amounts with a separate refunds table
Cross-referencing negative transaction amounts with a separate refunds table is a validation technique that confirms whether the negative values are legitimate refunds. It compares the dataset against an authoritative source to verify accuracy. This approach preserves data integrity and ensures that any subsequent analysis correctly accounts for refunds. Other options either summarize, alter, or delete data without confirming the values' meaning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Calculate the mean and standard deviation of transaction_amount
Why it's wrong here
Calculating descriptive statistics like mean and standard deviation summarizes the distribution but does not validate whether negative values are refunds. These statistics would be skewed by the negatives but provide no context about their origin. They cannot confirm if the values are legitimate or errors. Thus, this technique does not address the specific validation need of confirming refunds.
- ✗
Remove rows with negative transaction amounts
Why it's wrong here
Removing rows with negative amounts would discard potentially valid refund data, leading to incomplete analysis and biased results. This is a deletion strategy, not a validation method. It does not confirm whether the negatives are refunds or errors; it simply eliminates them. The analyst should first validate the values before deciding on any removal, making this choice premature and incorrect.
- ✗
Replace all negative values with zero
Why it's wrong here
Replacing negative values with zero would alter the data and potentially destroy legitimate refund information. This is a data cleaning action, not a validation technique, and it assumes the negatives are errors without evidence. It would introduce bias and inaccuracies. The analyst needs to confirm the nature of the values, not arbitrarily change them, so this approach is inappropriate.
- ✓
Cross-reference negative amounts with a separate refunds table
Why this is correct
Cross-referencing with a refunds table directly verifies whether negative transaction amounts correspond to legitimate refunds. This validation technique compares data across sources to confirm accuracy and meaning. It is the most reliable way to distinguish refunds from data entry errors. By matching transaction IDs or timestamps, the analyst can confirm the negative values are intentional and correctly recorded.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 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 DA0-002 exam.