DA0-002 Data Acquisition and Preparation Practice Question
A data analyst needs to identify duplicate customer records. Which TWO methods are commonly used? (Select two.)
⚠ Common exam trap
Watch out — candidates often choose 'Exact match on all fields' (Option E) thinking it is a reliable deduplication method, but in practice it fails to catch real-world duplicates that have any minor variation, and the exam expects you to recognize that fuzzy matching and sorted adjacency comparisons are the standard techniques for duplicate detection.
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
✓
Fuzzy matching using Levenshtein distance
Fuzzy matching using Levenshtein distance (Option A) is correct because it measures the edit distance between two strings, allowing identification of duplicates even when there are minor typographical differences, such as 'Jon Smith' vs. 'John Smith'. This is essential for deduplicating customer records where names, addresses, or other fields may have slight variations without being exact matches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fuzzy matching using Levenshtein distance
Why this is correct
Levenshtein distance catches spelling differences.
- ✓
Sorting and comparing adjacent rows
Why this is correct
Sorting groups potential duplicates together for efficient comparison.
- ✗
Visual inspection of random sample
Why it's wrong here
Not practical for large datasets.
- ✗
Using a hash function on primary key
Why it's wrong here
Primary keys are unique by definition, so hashing won't find duplicates.
- ✗
Exact match on all fields
Why it's wrong here
Exact match misses slight variations like typos.
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Written by Johnson Ajibi, MSc IT Security
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