SF-Data-Arch Master Data Management Practice Question
Which of the following is an example of 'Probabilistic Matching' in the context of an MDM solution?
⚠ Common exam trap
Candidates often confuse probabilistic matching with deterministic matching, selecting options that describe exact field-level comparisons instead of the fuzzy logic and statistical scoring that define probabilistic approaches.
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
✓
Comparing customer names and addresses using fuzzy logic to generate a match score.
Probabilistic matching uses statistical algorithms to determine the likelihood that two records refer to the same entity based on similarity rather than exact matches. This is vital in MDM because data across systems is rarely perfectly identical due to typos, variations, or formatting differences. Using algorithms allows the system to identify matches that deterministic logic would miss, significantly improving the completeness and accuracy of the resulting golden record.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Matching two records because they share the exact same Social Security Number.
Why it's wrong here
Matching based on a unique, exact identifier like an SSN is considered deterministic matching. It relies on a binary 'match' or 'no-match' outcome, rather than evaluating the probability of a match based on a weighted set of non-unique or potentially flawed attributes, which defines probabilistic matching.
- ✓
Comparing customer names and addresses using fuzzy logic to generate a match score.
Why this is correct
Probabilistic matching evaluates multiple fields for similarity, assigning weights and scores to reach a conclusion. By using fuzzy logic to account for variations like 'John Smith' versus 'Jon Smyth' at similar addresses, the system can identify matches that would otherwise be ignored by rigid, exact-match requirements.
- ✗
Using a strict SQL join condition to link Account records with Contact records.
Why it's wrong here
A SQL join is a deterministic operation that relies on explicit primary and foreign keys. There is no statistical evaluation or confidence score involved; the system strictly enforces the relationship based on the defined database schema, which is the opposite of the fuzzy, heuristic-based nature of probabilistic matching.
- ✗
Rejecting any contact record that lacks a valid email address field.
Why it's wrong here
Rejecting records based on missing data is a data quality validation rule, not a matching rule. It deals with data completeness rather than entity resolution, and does not involve any statistical comparison or probability scoring between different records to determine if they represent the same real-world customer.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Salesforce exam blueprint
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