This post is a part of the DP-800: Developing AI-Enabled Database Solutions Exam Prep Hub.
This topic falls under these sections:
Design and develop database solutions (35–40%)
   --> Write advanced T-SQL code
      --> Write queries that include fuzzy string matching functions, such as EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, and JARO_WINKLER_DISTANCE

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Introduction

Traditional string comparisons in SQL use operators such as = and LIKE, which require an exact or pattern-based match. However, real-world data is often inconsistent. Misspellings, abbreviations, typographical errors, and formatting differences frequently occur in customer names, product descriptions, addresses, emails, and other text fields.

To address these challenges, SQL Server 2025 (17.x) Preview and Azure SQL Database introduce native fuzzy string matching functions. These functions measure how similar two strings are rather than requiring them to match exactly.

For the DP-800: Developing AI-Enabled Database Solutions certification exam, understanding fuzzy matching is valuable because AI-enabled applications frequently work with imperfect or human-generated text. Fuzzy matching can improve search accuracy, data quality, duplicate detection, and entity matching.

The primary fuzzy matching functions include:

• EDIT_DISTANCE()
• EDIT_DISTANCE_SIMILARITY()
• JARO_WINKLER_DISTANCE()

These functions allow developers to compare strings and determine how closely they resemble one another.

Exam Note: These fuzzy matching functions are new capabilities introduced in SQL Server 2025 (17.x) Preview and Azure SQL Database. They represent Microsoft’s modern approach to intelligent text processing and may appear in newer versions of the DP-800 exam.

What is Fuzzy String Matching?

Fuzzy string matching compares two strings and determines how similar they are, even if they are not identical.

For example:

Unlike an equality comparison (=), fuzzy matching recognizes that many differences are minor typographical variations.

Why Fuzzy Matching Matters

Organizations often receive data from multiple sources:

• Customer registration forms
• Web applications
• Mobile apps
• AI chatbots
• OCR (Optical Character Recognition)
• Voice transcription
• External APIs
• CSV imports

These data sources often contain spelling mistakes or inconsistent formatting.

Examples include:

Traditional SQL comparisons fail to recognize these values as similar, whereas fuzzy matching functions can identify likely matches.

Understanding Edit Distance

The Edit Distance (also known as the Levenshtein distance) measures the minimum number of operations required to transform one string into another.

The allowed operations are:

• Insert a character
• Delete a character
• Replace a character

Example:

CAT
CUT

Only one substitution is required:

A → U

Edit distance = 1

Another example:

Microsoft
Microsft

Only one missing letter (“o”).

Edit distance = 1

The lower the edit distance, the more similar the strings.

EDIT_DISTANCE()

Purpose

Returns the minimum number of character edits required to convert one string into another.

Syntax

EDIT_DISTANCE(string1, string2)

Example

SELECT EDIT_DISTANCE(
    'Microsoft',
    'Microsft'
);

Output

1

Example

SELECT EDIT_DISTANCE(
    'Database',
    'Databaze'
);

Output

1

Example

SELECT EDIT_DISTANCE(
    'Azure',
    'Amazon'
);

Output

5

A larger number indicates the strings are less similar.

Common Uses of EDIT_DISTANCE()

• Duplicate customer detection
• Name matching
• Address matching
• Product matching
• AI-generated text validation
• OCR correction
• Search suggestions
• Data cleansing

EDIT_DISTANCE_SIMILARITY()

Purpose

Returns a similarity score rather than the number of edits.

Instead of measuring differences, this function measures similarity.

Syntax

EDIT_DISTANCE_SIMILARITY(
    string1,
    string2
)

The function returns a percentage-like similarity score.

Higher values indicate greater similarity.

Example

SELECT EDIT_DISTANCE_SIMILARITY(
    'Jonathan',
    'Jonathon'
);

Possible output

89

Example

SELECT EDIT_DISTANCE_SIMILARITY(
    'SQL Server',
    'SQL Server'
);

Output

100

Example

SELECT EDIT_DISTANCE_SIMILARITY(
    'Azure',
    'Amazon'
);

Possible output

20

Interpreting Similarity Scores

Developers commonly define thresholds depending on business requirements.

For example:

Similarity >= 90

might be considered an automatic match.

JARO_WINKLER_DISTANCE()

Purpose

Measures similarity using the Jaro-Winkler algorithm, which gives additional weight to matching prefixes.

This algorithm performs particularly well for:

• Person names
• Company names
• City names
• Street names

Because many spelling variations occur toward the end of words, Jaro-Winkler favors strings that begin similarly.

Example

John
Jon

Very high similarity.

Example

Jonathan
Jonathon

High similarity.

Example

Smith
Smyth

High similarity.

Syntax

JARO_WINKLER_DISTANCE(
    string1,
    string2
)

Example

SELECT JARO_WINKLER_DISTANCE(
    'Jonathan',
    'Jonathon'
);

Possible output

0.08

Lower values indicate the strings are more alike (with 0 representing an exact match).

Edit Distance vs. Jaro-Winkler

Real-World Business Scenarios

Customer Deduplication

John Smith
Jon Smith

Likely the same customer.

Product Matching

Surface Laptop
Surface Laptp

Typographical error.

Address Matching

123 Main Street
123 Main St.

Likely identical location.

OCR Cleanup

OCR software may read:

Micr0soft

instead of

Microsoft

Fuzzy matching helps identify the intended value.

AI Output Validation

Large language models occasionally generate slight variations:

SQL Sever

instead of

SQL Server

Fuzzy matching can detect likely errors before data is stored.

AI-Enabled Database Scenarios

These functions are especially useful in AI-powered database solutions.

Examples include:

• Matching chatbot responses to known products
• Detecting duplicate support tickets
• Matching customer names across systems
• Validating OCR-generated text
• Comparing AI-generated summaries
• Detecting near-duplicate documents
• Matching vector-search metadata
• Intelligent search suggestions
• Auto-correcting user input
• Identity resolution

Performance Considerations

Fuzzy matching functions perform more computation than exact string comparisons.

Best practices include:

• Filter data before applying fuzzy matching.
• Use indexes to reduce the number of candidate rows.
• Avoid comparing every row to every other row.
• Use similarity thresholds to eliminate weak matches.
• Test performance on production-sized datasets.
• Consider precomputing or caching similarity scores for frequently compared values.
• Use fuzzy matching only when exact matching is insufficient.

Best Practices

• Normalize text before comparison (trim spaces, consistent casing, remove unnecessary punctuation).
• Use exact matching whenever possible for better performance.
• Choose appropriate similarity thresholds for your business requirements.
• Use EDIT_DISTANCE() when you need the number of edits.
• Use EDIT_DISTANCE_SIMILARITY() when you need an intuitive similarity score.
• Use JARO_WINKLER_DISTANCE() for names and identity matching.
• Validate results before automatically merging records.
• Benchmark fuzzy matching against realistic datasets.

Common Exam Tips

Remember these key points for the DP-800 exam:

• Fuzzy matching compares similarity rather than exact equality.
• EDIT_DISTANCE() returns the number of edits needed to transform one string into another.
• Smaller edit distances indicate greater similarity.
• EDIT_DISTANCE_SIMILARITY() returns a normalized similarity score, where higher values represent more similar strings.
• JARO_WINKLER_DISTANCE() emphasizes matching prefixes and is particularly effective for comparing names.
• Fuzzy matching is useful for data quality, duplicate detection, AI-generated content validation, OCR cleanup, and intelligent search.
• Because fuzzy matching is computationally intensive, use it selectively and after narrowing the candidate set when possible.

Practice Exam Questions

Question 1

A company imports customer records from multiple systems. Which function is best suited to determine the minimum number of character changes required to transform one customer name into another?

A. EDIT_DISTANCE()

B. EDIT_DISTANCE_SIMILARITY()

C. JARO_WINKLER_DISTANCE()

D. LIKE

Answer: A

Explanation: EDIT_DISTANCE() calculates the minimum number of insertions, deletions, and substitutions needed to transform one string into another.

Question 2

Which fuzzy matching function returns a normalized similarity score where higher values indicate more similar strings?

A. REGEXP_LIKE()

B. JARO_WINKLER_DISTANCE()

C. EDIT_DISTANCE_SIMILARITY()

D. CHARINDEX()

Answer: C

Explanation: EDIT_DISTANCE_SIMILARITY() converts the edit distance into a similarity score, making it easier to establish matching thresholds.

Question 3

A database developer is comparing customer names such as “John” and “Jon.” Which function is generally most appropriate?

A. EDIT_DISTANCE()

B. PATINDEX()

C. LIKE

D. JARO_WINKLER_DISTANCE()

Answer: D

Explanation: Jaro-Winkler is particularly effective for comparing names because it gives additional weight to matching prefixes.

Question 4

What does an EDIT_DISTANCE() value of 0 indicate?

A. The strings are unrelated.

B. One string contains only numbers.

C. The strings are identical.

D. The comparison failed.

Answer: C

Explanation: An edit distance of zero means no insertions, deletions, or substitutions are required because the strings are identical.

Question 5

Which scenario is the best candidate for fuzzy string matching?

A. Comparing integer primary keys.

B. Matching customer names entered manually.

C. Sorting dates.

D. Calculating sales totals.

Answer: B

Explanation: Fuzzy matching is designed to compare imperfect text, such as names entered by users that may contain spelling variations.

Question 6

Why should fuzzy matching generally be applied after filtering candidate rows?

A. It prevents SQL injection.

B. It automatically creates indexes.

C. It reduces computational cost and improves query performance.

D. It guarantees exact matches.

Answer: C

Explanation: Fuzzy matching algorithms are more expensive than exact comparisons, so reducing the candidate set improves performance.

Question 7

Which statement about JARO_WINKLER_DISTANCE() is correct?

A. It counts the number of vowels in a string.

B. It gives additional weight to matching prefixes.

C. It replaces text using regular expressions.

D. It returns the number of character edits.

Answer: B

Explanation: The Jaro-Winkler algorithm favors strings that share the same beginning, making it particularly useful for matching names.

Question 8

Which of the following is a common AI-enabled use case for fuzzy string matching?

A. Creating clustered indexes.

B. Encrypting sensitive columns.

C. Detecting likely duplicate support tickets generated by AI systems.

D. Managing SQL Server backups.

Answer: C

Explanation: AI-generated text may contain slight wording differences, making fuzzy matching valuable for identifying duplicate or highly similar records.

Question 9

A similarity score of 100 returned by EDIT_DISTANCE_SIMILARITY() most likely indicates:

A. The strings are completely different.

B. The strings have five character differences.

C. The comparison failed.

D. The strings are identical.

Answer: D

Explanation: A score of 100 represents an exact match between the two strings.

Question 10

Which statement best describes fuzzy string matching?

A. It requires strings to be identical.

B. It compares the similarity between strings, even when they contain typographical errors.

C. It is designed exclusively for JSON processing.

D. It replaces SQL indexes.

Answer: B

Explanation: Fuzzy matching measures similarity rather than exact equality, making it useful for handling misspellings, abbreviations, and other textual variations.

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