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Salesforce Data-Cloud-Consultant Exam Sample Questions

Question # 1

A consultant wants to confirm the Identity resolution they Just set up. Which two features can the consultant use to validate the data on a unified profile? (Choose 2 answers)

A. Identity Resolution

B. Data Actions

C. Data Explorer

D. Query API

Answer

A. Identity Resolution
C. Data Explorer

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Question # 2

A customer notices that their consolidation rate is low across their account unification. They have mapped Account to the Individual and Contact Point Email DMOs. What should they do to increase their consolidation rate?

A. Change reconciliation rules to Most Occurring.

B. Disable the individual identity ruleset.

C. Increase the number of matching rules.

D. Update their account address details in the data source

Answer

C. Increase the number of matching rules.

Explanation:

Consolidation Rate: The consolidation rate in Salesforce Data Cloud refers to the effectiveness of unifying records into a single profile. A low consolidation rate indicates that many records are not being successfully unified.

Matching Rules: Matching rules are critical in the identity resolution process. They define the criteria for identifying and merging duplicate records.

Solution:

Increase Matching Rules: Adding more matching rules improves the system's ability to identify duplicate records. This includes matching on additional fields or using more sophisticated matching algorithms.

Steps:

Access the Identity Resolution settings in Data Cloud.
Review the current matching rules.
Add new rules that consider more fields such as phone number, address, or other unique identifiers.

Benefits:

Improved Unification: Higher accuracy in matching and merging records, leading to a higher consolidation rate.
Comprehensive Profiles: Enhanced customer profiles with consolidated data from multiple sources.

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Question # 3

What is the result of a segmentation criteria filtering on City | Is Equal To | 'San José'?

A. Cities containing 'San José’, 'San Jose’, 'san jose’, or 'san jose’

B. Cities only containing 'San Jose' or 'san jose’

C. Cities only containing 'San Jose' or 'San Jose'

D. Cities only containing 'San José’ or 'san josé'

Answer

D. Cities only containing 'San José’ or 'san josé'

Explanation: The result of a segmentation criteria filtering on City | Is Equal To | ‘San José’ is cities only containing 'San José’ or ‘san josé’. This is because the segmentation criteria is case-sensitive and accent-sensitive, meaning that it will only match the exact value that is entered in the filter1. Therefore, cities containing 'San Jose’, 'san jose’, or ‘San Jose’ will not be included in the result, as they do not match the filter value exactly. To include cities with different variations of the name ‘San José’, you would need to use the OR operator and add multiple filter values, such as ‘San José’ OR ‘San Jose’ OR ‘san jose’ OR 'san josé’2. 

References: Segmentation Criteria, Segmentation Operators

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Question # 4

A consultant wants to build a new audience in Data Cloud. Which three criteria can the consultant include when building a segment? Choose 3 answers

A. Direct attributes

B. Data stream attributes

C. Calculated Insights

D. Related attributes

E. Streaming insights

Answer

A. Direct attributes
C. Calculated Insights
D. Related attributes

Explanation:

A segment is a subset of individuals who meet certain criteria based on their attributes and behaviors. A consultant can use different types of criteria when building a segment in Data Cloud, such as:

Direct attributes: These are attributes that describe the characteristics of an individual, such as name, email, gender, age, etc. These attributes are stored in the Profile data model object (DMO) and can be used to filter individuals based on their profile data.

Calculated Insights: These are insights that perform calculations on data in a data space and store the results in a data extension. These insights can be used to segment individuals based on metrics or scores derived from their data, such as customer lifetime value, churn risk, loyalty tier, etc.

Related attributes: These are attributes that describe the relationships of an individual with other DMOs, such as Email, Engagement, Order, Product, etc. These attributes can be used to segment individuals based on their interactions or transactions with different entities, such as email opens, clicks, purchases, etc.

The other two options are not valid criteria for building a segment in Data Cloud. Data stream attributes are attributes that describe the streaming data that is ingested into Data Cloud from various sources, such as Marketing Cloud, Commerce Cloud, Service Cloud, etc. These attributes are not directly available for segmentation, but they can be transformed and stored in data extensions using streaming data transforms. Streaming insights are insights that analyze streaming data in real time and trigger actions based on predefined conditions. These insights are not used for segmentation, but for activation and personalization.

References: Create a Segment in Data Cloud, Use Insights in Data Cloud, Data Cloud Data Model

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Question # 5

Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use. Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?

A. Create new segments using nested segments.

B. Create a High Investment Balance calculated insight.

C. Package High Investment Balance Customers in a data kit.

D. Create new segments by cloning High Investment Balance Customers.

Answer

A. Create new segments using nested segments.

Explanation:

Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:

B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.

C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.

D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy.

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