The answer is sorting and comparing adjacent rows, along with fuzzy matching using Levenshtein distance. Sorting arranges records in a defined order, such as by customer name, so that potential duplicates appear next to each other; comparing adjacent rows then allows an analyst to quickly spot exact or near-identical entries. Fuzzy matching using Levenshtein distance measures the number of single-character edits needed to transform one string into another, making it ideal for catching duplicates with minor typos like 'Jon Smith' versus 'John Smith'. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of practical data cleaning techniques, often appearing in scenarios where customer records have inconsistent formatting or spelling errors. A common trap is to rely solely on exact matching, which misses variations, or to overlook the efficiency of sorting before comparison. Remember the tip: “Sort to spot, fuzzy to finesse” — sorting reveals obvious duplicates, while fuzzy matching catches the subtle ones.
DA0-001 Mining and Acquiring Data Practice Question
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 data analyst needs to identify duplicate customer records. Which TWO methods are commonly used? (Select two.)
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
Read the scenario before looking for a memorised answer.
✓
Sorting and comparing adjacent rows
Why this is correct
Sorting groups potential duplicates together for efficient comparison.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Levenshtein distance calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. In practice, a threshold (e.g., distance ≤ 2) is used to flag potential duplicates, and this method is often combined with blocking or indexing techniques to avoid O(n²) comparisons across millions of records. Sorting and comparing adjacent rows (Option B) works efficiently after sorting by a key like name or address, as it reduces the comparison space to O(n log n) for sorting plus O(n) for scanning, but it assumes that duplicates will appear consecutively after sorting, which may fail if sorting keys are not perfectly consistent.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Mining and Acquiring Data — This question tests Mining and Acquiring Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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