DA0-002 Data Analysis Practice Question
A data analyst is cleaning a customer dataset. Which two actions are appropriate for handling duplicate records? (Choose TWO)
⚠ Common exam trap
Watch out — candidates often confuse data-cleaning categories — candidates may pick mean imputation or Z-score standardization because they sound like 'cleaning' steps, but those address missing values and scaling, not duplicates.
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
✓
Remove all rows with identical values in every field
Option C is correct because removing rows whose values are identical across every field eliminates exact duplicate records, which is a standard and safe deduplication step in data cleaning. Option E is correct because fuzzy matching algorithms (e.g., Levenshtein distance or Jaro-Winkler similarity) identify near-duplicates that differ slightly due to typos, formatting, or abbreviations, allowing the analyst to review and consolidate them. Option A is incorrect because mean imputation addresses missing values, not duplicate records. Option B is incorrect because deleting every row with a duplicate email address is overly aggressive and may remove legitimate distinct customers who share an email. Option D is incorrect because Z-score standardization is a scaling technique for numeric features and does not handle duplicates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute missing values with mean
Why it's wrong here
Mean imputation fills nulls in a column; it does not detect or resolve duplicate records, so duplicates remain in the dataset. It is tempting because it is a standard cleaning action, and it would be correct when missing numeric values must be replaced to preserve row counts for analysis.
- ✗
Delete any row with a duplicate email address
Why it's wrong here
Deleting every row sharing an email address removes legitimate distinct customers who reuse an address, rather than resolving true duplicates; deduplication must match on a full record key. It is tempting because email looks unique, and this would be correct only where the business rule guarantees one account per address.
- ✓
Remove all rows with identical values in every field
Why this is correct
Exact-row deduplication removes records where every field matches, eliminating true duplicates while preserving legitimate distinct customers who happen to share some values. This directly satisfies the cleaning goal, since identical rows across all fields carry no additional information and inflate counts and aggregates.
- ✗
Apply Z-score standardization
Why it's wrong here
Z-score standardisation rescales numeric values to a common distribution; it neither identifies nor removes duplicate rows, leaving the duplicates intact. It is tempting because it is a legitimate data-preparation step, and it would be correct when numeric features with differing scales must be normalised before distance-based modelling.
- ✓
Use a fuzzy matching algorithm to identify near-duplicates
Why this is correct
Fuzzy matching identifies near-duplicates such as "Jon Smith" versus "John Smith", catching records exact comparison misses due to typos, casing or formatting variation. This satisfies the cleaning goal by surfacing probable duplicates for review, rather than silently deleting records that differ in some fields.
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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.