Data 360 Consultant Identity Resolution Practice Test 6 — 15 Questions

Question 1

Which THREE attributes can be leveraged in fuzzy matching rules within Salesforce Data Cloud Identity Resolution? Choose 3 options.

Accepted Answer

Customer Name (First and Last Name). Fuzzy matching rules support attributes such as name, email, and address/postal details.

Answer

Activation Target ID

Answer

Calculated Insight KPI Value

Question 2

Which THREE actions should a consultant take when auditing an identity resolution implementation where records are incorrectly merging across distinct customer households? Choose 3 options.

Accepted Answer

Incorporate high-cardinality unique identifiers like distinct account numbers or verified emails.. Incorrect cross-household merging (over-merging) requires reviewing broad match rules, adding secondary criteria with strict AND logic, and verifying unique identifiers.

Answer

Switch all reconciliation rules from Most Recent to Longest String.

Answer

Delete all unified link records manually via Apex.

Question 3

Which THREE behaviors characterize how Data Cloud processes reconciliation rules during identity resolution? Choose 3 options.

Accepted Answer

Different reconciliation strategies can be assigned to different attributes (e.g., Most Recent for phone, Source Sequence for name).. Reconciliation rules determine surviving attribute values, operate after record matching occurs, and can be customized per attribute field.

Answer

They automatically overwrite and update records in the source CRM system.

Answer

They determine whether two source records are permitted to match based on email addresses.

Question 4

Which TWO benefits are provided by mapping source data to standard Data Model Objects (DMOs) before identity resolution? Choose 2 options.

Accepted Answer

Facilitates automatic data normalization and formatting during processing.. Standard DMO mapping enables out-of-the-box matching rules and automatic data normalization.

Answer

Deletes duplicate source records from external source systems.

Answer

Provision new user permission sets automatically.

Answer

Automatically exports unified profiles to external data lakes.

Question 5

Which THREE troubleshooting steps should a consultant take when identity resolution execution results in zero unified profiles being generated? Choose 3 options.

Accepted Answer

Review match rule definitions to ensure they are not overly restrictive or incorrectly configured.. Option B is correct because identity resolution depends on match rules; if the rules are overly restrictive (for example, requiring exact matches on multiple fields or using incorrect match criteria), no records will satisfy them and zero unified profiles will be created, so reviewing and loosening or correcting the rules is a valid troubleshooting step. Option D is correct because identity resolution requires source data to be mapped into the Individual and Contact Point DMOs; if data model mappings are missing or incorrect, the required attributes and contact points never reach the objects the match rules evaluate, resulting in no matches. Option E is correct because if data streams are not actively ingesting records or the source DMOs contain no data, there is nothing for identity resolution to process, so verifying ingestion status and data presence is essential. Option A does not belong because activation target polling frequency affects outbound activation jobs, not the identity resolution process that generates unified profiles. Option C does not belong because deleting data spaces and reinstalling the Data Cloud package is a destructive, disproportionate action that would not address the typical configuration or data causes of zero unified profiles and could cause data loss.

Answer

Increase the activation target polling frequency.

Answer

Delete all data spaces and reinstall the Data Cloud package.

Question 6

Which TWO statements are true regarding the relationship between Individual DMOs and Unified Individual DMOs? Choose 2 options.

Accepted Answer

Source individual records are mapped into the Individual DMO.. Source individual records map to the Individual DMO, and identity resolution links multiple source individuals into a single Unified Individual DMO.

Answer

Unified Individual profiles are stored directly in core Salesforce CRM user tables.

Answer

The Individual DMO is populated directly by activation targets.

Answer

The Individual DMO cannot be used in identity resolution rulesets.

Question 7

Which THREE configuration options must be validated when setting up a Source Sequence reconciliation rule? Choose 3 options.

Accepted Answer

Ensuring participating data sources are correctly identified and mapped in the ruleset scope.. Option C is correct because a Source Sequence rule only works when the participating data sources are properly identified and mapped within the ruleset scope, so the engine knows which sources contribute to the attribute. Option D is correct because the Source Sequence reconciliation rule must be explicitly selected for the specific attribute being reconciled; otherwise the attribute will use a different reconciliation strategy. Option E is correct because Source Sequence relies on a defined priority order of the contributing data sources, which determines which source's value wins when values conflict. Option A is not required, since Source Sequence does not depend on custom SQL string-length calculations. Option B is not required, because OAuth token configuration for external S3 buckets is unrelated to defining a Source Sequence reconciliation rule.

Answer

Writing a custom SQL query to calculate string lengths.

Answer

Configuring OAuth authentication tokens for external S3 buckets.

Question 8

Which THREE strategies should an enterprise architect recommend to maintain high data hygiene and prevent profile fragmentation in Data Cloud Identity Resolution? Choose 3 options.

Accepted Answer

Design hierarchical match rules moving from high-confidence exact matches to controlled fuzzy matches.. Option A is correct because designing hierarchical match rules that start with high-confidence exact matches (e.g., exact email or exact CRM ID) and only then fall back to controlled fuzzy matches (e.g., fuzzy name plus normalized phone) reduces false positives and prevents unintended profile merges that cause fragmentation. Option B is correct because standardizing contact point formatting (e.g., normalizing phone numbers to E.164, lowercasing emails, trimming whitespace) and enforcing data quality at ingestion ensures match rules operate on consistent values, which is the foundation of reliable Identity Resolution in Data Cloud. Option E is correct because regularly auditing match rules and unified profile metrics (such as match rate, merge rate, and profile counts) surfaces over-merging or under-merging trends early, allowing architects to tune rulesets before fragmentation spreads. Option C is incorrect because disabling identity resolution rulesets and relying only on manual segmentation filters removes automated unification and guarantees profile fragmentation. Option D is incorrect because manually deleting unified link records nightly with data transform SQL destroys the unified profile graph and is not a supported or sustainable hygiene strategy.

Answer

Disable identity resolution rulesets and rely exclusively on manual segmentation filters.

Answer

Manually delete unified link records every night using data transform SQL.

Question 9

A consultant is building an Identity Resolution ruleset for a retail company. The business wants to avoid creating a unified profile when two individuals share only the same last name and postal code, because this combination is too common and causes false positives. However, the business still wants to match records that share an exact email address. How should the consultant configure the match rules to best meet these requirements?

Accepted Answer

Create a match rule for exact email and set the ruleset to use any match rule, but do not create a match rule for last name and postal code.. The business wants to unify records based on exact email while preventing unification based solely on last name and postal code. Defining only an exact email match rule and using any match rule achieves this, because records will only be unified when they share an exact email. No match rule for the common combination means that combination will never trigger a unified profile, eliminating the false positives the business wants to avoid.

Answer

Create a match rule for exact email and a separate match rule for last name and postal code, and set the ruleset to use any match rule to unify records.

Answer

Create a match rule for last name and postal code and set the ruleset to require all match rules to be satisfied, and add an exact email match rule as a secondary condition.

Answer

Create a match rule for exact email and set the ruleset to require all match rules to be satisfied before creating a unified profile.

Question 10

A consultant is configuring an Identity Resolution ruleset for a telecom company. The business wants to ensure that when multiple source profiles are matched, the unified profile's 'Loyalty Tier' field is populated with the value from the most recently updated source record. Which type of rule should the consultant configure to achieve this?

Accepted Answer

Reconciliation Rule. Reconciliation rules determine how attribute values are selected when multiple source profiles are unified. The 'Most Recent' strategy picks the value from the most recently updated source record. Match rules identify duplicates, but they do not resolve attribute conflicts. Data spaces and fuzzy matching serve different purposes. Thus, a reconciliation rule is the correct component to ensure the Loyalty Tier reflects the latest update.

Answer

Match Rule with Fuzzy Matching

Answer

Data Space Rule

Answer

Match Rule

Question 11

A consultant is designing an Identity Resolution ruleset for a healthcare organization that needs to match patients based on a combination of last name, date of birth, and a government-issued identifier. The government identifier is only present in 60% of the source records. Which match rule configuration should the consultant use to maximize match accuracy while avoiding false positives?

Accepted Answer

Create two match rules: one requiring exact match on government identifier, and another requiring exact match on last name and date of birth.. A multi-rule approach balances precision and recall. The government identifier provides high-confidence matches when present, while the combination of last name and date of birth serves as a reliable fallback for records missing the identifier. This strategy avoids the false positives that fuzzy matching might introduce and ensures broader coverage than relying on the identifier alone. It is the most robust configuration for the described dataset.

Answer

Create a single match rule that requires exact match on government identifier only, and rely on reconciliation for the rest.

Answer

Create a single match rule that requires exact matches on last name, date of birth, and government identifier.

Answer

Create a single match rule that uses fuzzy matching on last name and exact match on date of birth, ignoring the government identifier.

Question 12

A consultant needs fuzzy matching on mailing addresses for a ruleset where source addresses are inconsistently formatted, including abbreviations such as 'St' versus 'Street' and missing directional prefixes. Which match rule configuration should the consultant choose?

Accepted Answer

A fuzzy match rule on the normalized Address field with an appropriate fuzzy tolerance value. Fuzzy match rules compare normalized address values and tolerate formatting differences through a tunable tolerance. That combination handles abbreviations and missing directional prefixes while still rejecting genuinely different addresses. Exact matching on either raw or unified fields cannot absorb the variation, and a fuzzy rule with zero tolerance behaves like an exact rule, so none of those alternatives satisfy the requirement.

Answer

A fuzzy match rule on the raw Address field with a fuzzy tolerance of zero

Answer

An exact match rule on the raw Address field from each individual source data stream

Answer

An exact match rule on the normalized Address field in the Unified Individual object

Question 13

An administrator is configuring Identity Resolution in Salesforce Data Cloud and needs to ensure that the unified profile includes the most complete mailing address. The source systems provide address data with varying levels of completeness. Which reconciliation rule strategy should the administrator select for the address fields?

Accepted Answer

Longest Value. Reconciliation rules in Salesforce Data Cloud support strategies like Most Recent, Source Priority, Most Frequent, and Longest Value. For completeness of address data, Longest Value is appropriate because it tends to pick the most detailed string. The other strategies do not evaluate completeness: Most Recent depends on timestamps, Source Priority on source ranking, and Most Frequent on occurrence count. Thus, Longest Value is the correct choice.

Answer

Source Priority

Answer

Most Recent

Answer

Most Frequent

Question 14

A consultant must ensure that when two source profiles are unified, the surviving Email Address value comes from the CRM stream rather than the marketing automation stream, regardless of which record arrived most recently. Where should this preference be configured?

Accepted Answer

In the reconciliation rule for the Email Address field, ordered by source priority. Reconciliation rules decide which source value populates a unified field when sources conflict. Ranking the CRM stream above marketing automation for Email Address makes the CRM value deterministic regardless of recency. Match rules and cardinality only control unification, and refresh schedules only control data arrival, so none of those alternatives can enforce the required source preference.

Answer

In the data stream refresh schedule for the CRM and marketing automation streams

Answer

In the identity resolution ruleset's Match All cardinality setting

Answer

In the match rule that compares Email Address across streams

Question 15

A consultant is explaining the Identity Resolution process to a client. The client wants to understand what happens after match rules identify potential matches. Which statement best describes the role of reconciliation rules in Salesforce Data Cloud?

Accepted Answer

Reconciliation rules define the logic for merging field values from matched records into a unified profile.. Reconciliation rules are used after match rules have identified that two or more source records refer to the same individual. Their purpose is to determine which value to keep for each field in the unified profile when the matched records have conflicting values. This ensures that the unified profile has a single, coherent set of attributes based on business rules such as most recent or highest priority.

Answer

Reconciliation rules assign a confidence score to each match to determine if a unified profile should be created.

Answer

Reconciliation rules determine which source records are considered for matching based on data quality.

Answer

Reconciliation rules define the schedule for running the Identity Resolution batch job.