Data 360 Consultant Identity Resolution Practice Test 1 — 15 Questions

Question 1

A consultant is setting up a match rule using Party Identification. Which condition must be met for this match rule to function correctly?

Accepted Answer

Party Identification rules only operate when exact email matching is disabled.. Party Identification match rules require the Party Identification DMO to be mapped with both the identification number and the identification type/name.

Answer

The Party Identification object must be manually joined to the Account object via SQL in Calculated Insights.

Answer

The Party Identification data must be ingested solely from Marketing Cloud data streams.

Question 2

A consultant is setting up match rules in a Data Cloud ruleset and wants to use a phone number match. Which standard match rule type should be selected?

Accepted Answer

Exact Phone. Exact Phone is the standard match rule type used for evaluating telephone number attributes.

Answer

Party Phone Identification

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Fuzzy Phone

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Contact Point String Match

Question 3

During an identity resolution audit, a consultant discovers that a significant number of unified profiles contain blank first names even though the contributing source records populated valid first names. What is the root cause of this issue?

Accepted Answer

The Source Sequence rule placed a data source with blank first name values at the top of the priority list.. If the selected reconciliation rule evaluates to null due to rule ordering or if an excluded/unpopulated source is prioritized in Source Sequence, surviving attributes can end up blank.

Answer

Data Cloud automatically scrubs all first names for GDPR compliance during identity resolution.

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The Individual DMO primary key was mapped incorrectly, deleting the first name column.

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Fuzzy name matching strips out all text strings containing vowels.

Question 4

A consultant is configuring identity resolution and needs to ensure that when multiple sources provide a customer's birthdate, the oldest recorded birthdate is retained on the unified profile. Which reconciliation rule should be used?

Accepted Answer

Source Sequence configured with the oldest system prioritized first.. While standard reconciliation rules include Most Recent, Source Sequence, Highest Frequency, and Longest String, specific attribute survivorship needs or custom logic may require understanding standard options. If oldest date is required, consultants should note that 'Most Recent' is standard, but let's evaluate the available choices.

Answer

Longest String

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Highest Frequency

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Most Recent

Question 5

Which DMO represents the relationship between source contact points and unified individuals?

Accepted Answer

Unified Link Contact Point objects. Unified Link objects bridge source entities to unified profiles.

Answer

Individual Source DMO

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Activation Target DMO

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Calculated Insight DMO

Question 6

A consultant discovers that an identity resolution ruleset is failing to link records because a custom identifier field was mapped to a standard field incorrectly. What is the correct procedure to fix this data modeling error and update the unified profiles?

Accepted Answer

Correct the data model mapping in Data Model setup, then run a manual refresh of the identity resolution ruleset.. Fixing data mapping requires updating the data model mapping, re-ingesting or refreshing data streams, and then re-running the identity resolution ruleset.

Answer

Create a secondary ruleset with conflicting match rules.

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Write a Calculated Insight to override the data mapping error.

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Delete the entire Data Cloud instance and provision a new data space.

Question 7

Which THREE reconciliation rule options are available when configuring attribute survivorship in a Data Cloud ruleset? Choose 3 options.

Accepted Answer

Most Recent. In Data Cloud's reconciliation ruleset, attribute survivorship supports three rule types: Most Recent (B), which selects the value from the record with the latest timestamp in a designated date field; Source Sequence (D), which picks the value from the highest-priority source according to a defined source order; and Longest String (E), which chooses the value containing the most characters. These three are the standard reconciliation rule options available for resolving conflicting attribute values during identity resolution. Alphabetical Ascending (A) is not a supported survivorship rule, since Data Cloud does not sort values alphabetically to determine the winner. Lowest Character Index (C) is also not available; there is no rule that selects a value based on character position within a string.

Answer

Alphabetical Ascending

Answer

Lowest Character Index

Question 8

A consultant is building a ruleset where exact matches on a Loyalty ID should be trusted as the strongest signal, but the business also wants to keep the match engine from merging records that merely share a common last name. Which ruleset setting controls how many match rules must all be satisfied before two source profiles are unified?

Accepted Answer

The 'Match Rules' cardinality setting (Match All vs. Match Any) on the ruleset. The ruleset's cardinality setting determines whether a unified profile requires all match rules to be satisfied or just one. Requiring all rules lets the strong Loyalty ID rule coexist with weaker rules without allowing last-name-only merges. Reconciliation rules act after merging, fuzzy tolerance tunes individual rules, and data spaces govern access, so none of those alternatives controls the number of satisfied rules needed.

Answer

The 'Match Rule' threshold value expressed as a percentage of fuzzy tolerance

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The 'Reconciliation Rule' priority ordering applied to the unified profile

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The 'Data Space' assignment applied to the source data streams

Question 9

A consultant is configuring Identity Resolution for a healthcare organization that receives patient data from multiple sources. The organization needs to ensure that when two records are matched, the unified profile retains the most recent address from the source system that has the highest priority. The consultant has already set up match rules and reconciliation rules. Which additional configuration is required to enforce source priority for address reconciliation?

Accepted Answer

Define a source priority order in the Identity Resolution ruleset settings.. Source priority order within the Identity Resolution ruleset is the mechanism that determines which source's value is retained when reconciliation rules are applied. By defining the priority, the system knows to prefer the highest-priority source's address, and if that source has a more recent address, it will be selected according to the reconciliation rule logic. This satisfies the requirement without custom development.

Answer

Set the address field as the primary key in the Unified Individual object to force selection from the highest-priority source.

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Create a custom formula field on the Unified Individual object that selects the address from the highest-priority source.

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Configure a data transform in the data stream to overwrite the address with the highest-priority source value before identity resolution runs.

Question 10

A consultant is configuring an Identity Resolution ruleset in Data Cloud. The business requires that when two source profiles are matched, the unified profile must retain the value from the record with the most recent last-updated timestamp for the 'Loyalty_Tier__c' field. Which ruleset component should the consultant configure to satisfy this requirement?

Accepted Answer

Reconciliation Rules. Reconciliation Rules control value selection when matched source profiles disagree. Choosing the most-recent strategy for Loyalty_Tier__c ensures the unified profile reflects the latest update. Match Rules only decide linkage, while Data Spaces and source priority do not evaluate record timestamps, so they cannot satisfy the stated requirement.

Answer

Source Priority Rules

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Data Space Assignment

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Match Rules

Question 11

A consultant is reviewing an Identity Resolution ruleset that uses a Fuzzy Name match rule and an Exact Email match rule. The consultant wants to ensure that the ruleset is optimized for performance and accuracy. Which two statements are true regarding the configuration of match rules in Salesforce Data Cloud? (Choose two.)

Accepted Answer

Fuzzy match rules are more resource-intensive than exact match rules and can impact batch processing time.. Fuzzy match rules are indeed more resource-intensive due to complex string comparisons, which can slow batch processing. Additionally, the choice between 'any' and 'all' logic for match rules significantly affects the number of unified profiles created, as it determines how strictly records must match. These two statements are accurate and important for optimizing identity resolution performance and accuracy.

Answer

Match rules are evaluated in the order they are listed, and the first match determines the unified profile.

Answer

Fuzzy match rules require an exact match on at least one other field to prevent false positives.

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Exact match rules are case-sensitive and will not match 'John@example.com' with 'john@example.com'.

Question 12

A consultant is troubleshooting an Identity Resolution job that is not matching records as expected. The ruleset includes a match rule on 'Email Address' using exact match. Upon investigation, the consultant finds that email addresses are stored in different cases (e.g., 'John.Doe@Example.com' vs 'john.doe@example.com'). What should the consultant do to ensure these records match?

Accepted Answer

Enable case-insensitive matching on the Email Address field in the match rule.. Exact match rules are case-sensitive by default in Salesforce Data Cloud Identity Resolution. To match email addresses that differ only in casing, the consultant should enable case-insensitive matching for that field within the match rule. This is a configuration setting that directly addresses the problem. Fuzzy matching, data normalization, or additional rules are either overbroad, require upstream changes, or do not solve the case sensitivity issue.

Answer

Change the match rule to use fuzzy matching on Email Address.

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Normalize all email addresses to lowercase in the source data before ingestion.

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Add a second match rule with exact match on a different field, such as Phone Number.

Question 13

A consultant is troubleshooting a ruleset where the unified profile count is far lower than expected. Which two configuration conditions most plausibly suppress legitimate unifications? (Choose two.)

Accepted Answer

A fuzzy match rule's tolerance is set so strictly that minor formatting differences prevent a match. Match All cardinality combined with a sparsely populated attribute forces every pair to satisfy a rule that frequently fails, and an overly strict fuzzy tolerance turns a rule into near-exact matching that rejects trivial variations. Both conditions prevent valid pairs from unifying and lower the unified profile count. Reconciliation ordering, data space access, and a consistently populated rule do not suppress unification.

Answer

The source data streams are assigned to a data space that the running user can access

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The reconciliation rule for Phone Number is ordered by source priority rather than by last-updated timestamp

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The ruleset includes a match rule on a field that is mapped in every source stream with consistent values

Question 14

A consultant is troubleshooting an Identity Resolution ruleset that is expected to match individuals who share the same normalized email address. After the initial run, the consultant finds that many records with identical email values were not unified. The email field is mapped in the source Data Streams and the match rule uses a Fuzzy Email match method with a low threshold. What is the most likely cause?

Accepted Answer

The source email values were not normalized before matching, so case or whitespace differences prevented unification.. Identity Resolution compares normalized values, so inconsistent normalization of email addresses can cause records that appear identical to be treated as different. Case, whitespace, or formatting differences can prevent fuzzy matching from reaching the threshold. Correcting normalization or adjusting the match method resolves the partial unification.

Answer

The match rule was configured with a high match threshold that excludes exact matches.

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The email field was not selected as a match criteria field in the ruleset.

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The ruleset was run in a sandbox that does not support Identity Resolution batch jobs.

Question 15

A consultant is setting up Identity Resolution for a retail company. The company wants to match individuals based on multiple attributes to improve accuracy. Which two match rule configurations are valid and can be used together in a single ruleset? (Choose two.)

Accepted Answer

Exact match on Loyalty ID. Valid match rules can combine multiple fields with different match methods. Exact match on Loyalty ID is valid and precise. Fuzzy match on First Name combined with exact match on Last Name is also valid, allowing for common name variations while maintaining some precision. Fuzzy matching on numeric fields like phone numbers or unique identifiers like Loyalty ID is not supported or advisable. Reconciliation strategies such as 'Most Recent' are not match methods. Thus, the two valid configurations are exact match on Loyalty ID and the combination of fuzzy First Name with exact Last Name.

Answer

Exact match on Email Address and Fuzzy match on Phone Number

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Exact match on Postal Code and Exact match on City

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Match on Date of Birth using 'Most Recent' strategy