DA0-002 Data Acquisition and Preparation Practice Question
A company is merging two customer databases from different acquisitions. They need to identify duplicate records. Which data profiling technique is most effective?
⚠ Common exam trap
Candidates often assume exact matching or primary keys are sufficient for deduplication, overlooking the real-world data inconsistencies that fuzzy matching is designed to handle.
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 on name and address
Fuzzy matching on name and address is the most effective technique because customer databases from different acquisitions often contain variations in spelling, formatting, and abbreviations (e.g., 'Bob' vs. 'Robert', 'St.' vs. 'Street'). Exact matching would miss these duplicates, while fuzzy matching uses algorithms like Levenshtein distance or Jaro-Winkler to quantify similarity and identify near-matches, ensuring comprehensive deduplication.
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 on name and address
Why this is correct
Fuzzy matching tolerates the typos, abbreviations and formatting variations that plague merged customer records, so near-identical names and addresses still score as matches. Exact matching would miss these, and the technique directly satisfies the requirement to identify duplicate records across the two acquisitions.
- ✗
Manually compare all records
Why it's wrong here
Manual comparison cannot scale to merged acquisition datasets, where record volumes and inconsistent formats make human review impractical and error-prone. It is tempting because eyeballing a small sample feels thorough, and manual review would suit a handful of records where judgement about fuzzy matches matters more than throughput.
- ✗
Exact match on customer names
Why it's wrong here
Exact name matching fails because the same customer is often recorded with variations, nicknames, or typos across acquisitions, so genuine duplicates are missed. It is tempting because it is trivial to implement and would be correct where names are standardised and verified, such as matching against a single cleansed reference list.
- ✗
Use primary keys from each database
Why it's wrong here
Primary keys are unique only within their own database, so the same customer can hold different keys in each source and matching on them finds no duplicates. It is tempting because keys are the natural join column within a single system, and they would be the right choice when reconciling records that already share a common identifier.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.