Quick Answer
The answer is fuzzy matching on name and address. This technique is the most effective for merging customer databases from different acquisitions because it accounts for variations in spelling, formatting, and abbreviations—such as “Bob” versus “Robert” or “St.” versus “Street”—that exact matching would miss. Fuzzy matching uses algorithms like Levenshtein distance or Jaro-Winkler to quantify similarity, enabling the identification of near-matches and ensuring comprehensive deduplication. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of data profiling techniques for real-world data quality challenges; a common trap is choosing exact matching, which fails with inconsistent data. Remember the memory tip: “Fuzzy finds the family, exact loses the cousin”—fuzzy matching catches the variations that exact matching overlooks.
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.
A company is merging two customer databases from different acquisitions. They need to identify duplicate records. Which data profiling technique is most effective?
Fuzzy matching on name and address
Fuzzy matching handles variations and is appropriate for deduplication.
Manually compare all records
Why wrong: Manual review is time-consuming and error-prone for large datasets.
Exact match on customer names
Why wrong: Exact match misses duplicates with minor name differences.
Use primary keys from each database
Why wrong: Primary keys are system-specific and won't match across databases.
Answer choices
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.
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.
Fuzzy matching on name and address
Why this is correct
Fuzzy matching handles variations and is appropriate for deduplication.
Related concept
Read the scenario before looking for a memorised answer.
Manually compare all records
Why it's wrong here
Manual review is time-consuming and error-prone for large datasets.
Exact match on customer names
Why it's wrong here
Exact match misses duplicates with minor name differences.
Use primary keys from each database
Why it's wrong here
Primary keys are system-specific and won't match across databases.
Common exam traps
The trap here is that candidates assume exact matching or primary keys are sufficient for deduplication, overlooking the real-world data inconsistencies that fuzzy matching is designed to handle.
Detailed technical explanation
Fuzzy matching typically relies on string similarity metrics such as Levenshtein distance (edit distance) or the Jaro-Winkler algorithm, which adjusts for common prefix matches. In real-world scenarios, combining multiple fields (e.g., name, address, phone) with weighted scoring reduces false positives, and blocking techniques (e.g., sorting by zip code) improve performance by limiting comparisons to candidate pairs.
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Mining and Acquiring Data — study guide chapter
Learn the concepts, then practise the questions
Mining and Acquiring Data practice questions
Targeted practice on this topic area only
All DA0-001 questions
509 questions across all exam domains
CompTIA Data+ DA0-001 study guide
Full concept coverage aligned to exam objectives
DA0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise DA0-001 questions linked to Comparing and Contrasting Data Concepts.
Practise DA0-001 questions linked to Mining and Acquiring Data.
Practise DA0-001 questions linked to Analyzing and Modeling Data.
Practise DA0-001 questions linked to Visualizing Data.
Practise DA0-001 questions linked to Communicating Data Insights.
Practise DA0-001 questions linked to CompTIA A+ hardware.
Practise DA0-001 questions linked to CompTIA A+ mobile devices.
Practise DA0-001 questions linked to CompTIA A+ networking.
Practise DA0-001 questions linked to CompTIA A+ operating systems.
Practise DA0-001 questions linked to CompTIA A+ security.
Practise DA0-001 questions linked to CompTIA A+ software troubleshooting questions.
Practise DA0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: 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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.