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| Vendor: | Salesforce |
|---|---|
| Exam Code: | Data-Cloud-Consultant |
| Exam Name: | Salesforce Certified Data Cloud Consultant (old) |
| Exam Questions: | 170 |
| Last Updated: | February 25, 2026 |
| Related Certifications: | Salesforce Consultant |
| Exam Tags: | Consultant Level Salesforce Data Cloud ConsultantsSalesforce Data Cloud Architects |
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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?
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.
Reference:
Salesforce Data Cloud Identity Resolution
Salesforce Help: Matching Rules
A Data Cloud consultant is evaluating the initial phase of the Data Cloud lifecycle for a company.
Which action is essential to effectively begin the Data Cloud lifecycle?
Data Cloud Lifecycle: The initial phase of the Salesforce Data Cloud lifecycle is critical for setting the foundation for successful data integration and utilization.
Identifying Use Cases:
Importance: Defining clear use cases helps in understanding the business objectives and how Data Cloud can address them.
Required Data Sources: Identifying the necessary data sources ensures that relevant data is ingested into Data Cloud.
Data Quality: Assessing data quality is essential for accurate and reliable data analysis and insights.
Actions:
Step 1: Engage with stakeholders to define specific use cases for Data Cloud.
Step 2: Identify and catalog the required data sources for these use cases.
Step 3: Evaluate the quality of data from these sources to ensure they meet the standards for effective data analysis.
Reference:
Salesforce Data Cloud Implementation Guide
Salesforce Data Cloud Lifecycle
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone,
and work phone as three separate fields for its customers on the Contact record. The company plans
to use Data Cloud and ingest the Contact object via the CRM Connector.
What is the most efficient approach that a consultant should take when ingesting this data to ensure
all the different phone numbers are properly mapped and available for use in activation?
The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point. This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers.
A user wants to be able to create a multi-dimensional metric to identify unified individual
lifetime value (LTV).
Which sequence of data model object (DMO) joins is necessary within the calculated Insight to
enable this calculation?
During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?
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