SF-Data-Arch Master Data Management Practice Question
A data architect at a global manufacturer is defining the match strategy for a new MDM hub. Legal names vary widely across regions, so the architect needs a technique that will correctly link records such as 'Acme Corp.' and 'Acme Corporation Ltd.' even though the strings differ. Which matching approach best satisfies this requirement?
⚠ Common exam trap
Candidates often confuse blocking with matching and assuming that any name-based rule will handle legal-name variants when only a similarity-based scorer can.
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
✓
Probabilistic (fuzzy) matching using token-based similarity scoring with a configurable match threshold.
The requirement is about linking records whose names differ lexically but refer to the same real-world entity. Deterministic rules and identifier-only matching both fail when the input strings vary, while blocking is a performance layer rather than a matching algorithm. Probabilistic scoring with a tunable threshold is the standard MDM technique for this kind of fuzzy corporate-name reconciliation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deterministic matching on the exact normalized Account Name field using a case-insensitive comparison.
Why it's wrong here
Case-insensitive exact matching would still treat 'Acme Corp.' and 'Acme Corporation Ltd.' as different values because the tokens and suffixes differ. It only collapses trivial variations like capitalization, so it cannot satisfy a requirement explicitly built around legal-name variations. This approach typically produces high precision but very low recall on the fuzzy corporate names described.
- ✓
Probabilistic (fuzzy) matching using token-based similarity scoring with a configurable match threshold.
Why this is correct
Probabilistic matching compares attributes using similarity algorithms such as Jaro-Winkler or token-based edit distance, then scores candidate pairs against a threshold. This is designed precisely for cases where legal names are semantically the same but lexically different, as with 'Acme Corp.' versus 'Acme Corporation Ltd.' It trades some precision for much higher recall, which matches the stated requirement.
- ✗
Blocking on the first three characters of the account name followed by exact comparison of the remaining characters.
Why it's wrong here
Blocking is a performance technique that partitions records into candidate groups so that fewer pair comparisons are required, not a matching method on its own. Pairing it with exact comparison still rejects 'Acme Corp.' versus 'Acme Corporation Ltd.' because the strings are not identical. Blocking would be a useful complement to a fuzzy scorer, but by itself it does not solve the variation problem described.
- ✗
Matching on the external ERP customer number only, ignoring name fields entirely during the match.
Why it's wrong here
Relying solely on a cross-system identifier assumes every source already carries the same canonical key, which is rarely true during initial consolidation and is not guaranteed by the scenario. It would fail to link records where the ERP number is missing or was assigned independently, and it provides no mechanism for the legal-name variations that the architect was asked to handle.
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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
This SF-Data-Arch practice question is part of Courseiva's free Salesforce 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 SF-Data-Arch exam.