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Data-Cloud-Consultant Practice Questions

Question # 1
What does the Source Sequence reconciliation rule do in identity resolution?
A. Includes data from sources where the data is most frequently occurring
B. Identifies which individual records should be merged into a unified profile by setting a priority for specific data sources
C. Identifies which data sources should be used in the process of reconcillation by prioritizing the most recently updated data source
D. Sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name


D. Sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name

Explanation: The Source Sequence reconciliation rule sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name. This rule allows you to define which data source should be used as the primary source of truth for each attribute, and which data sources should be used as fallbacks in case the primary source is missing or invalid. For example, you can set the Source Sequence rule to use data from Salesforce CRM as the first priority, data from Marketing Cloud as the second priority, and data from Google Analytics as the third priority for the first name attribute. This way, the unified profile will use the first name value from Salesforce CRM if it exists, otherwise it will use the value from Marketing Cloud, and so on. This rule helps you to ensure the accuracy and consistency of the unified profile attributes across different data sources. 
References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution, Reconciliation Rules

Question # 2
A retailer wants to unify profiles using Loyalty ID which is different than the unique ID of their customers. Which object should the consultant use in identity resolution to perform exact match rules on the Loyalty ID?
A. Party Identification object
B. Loyalty Identification object
C. Individual object
D. Contact Identification object


A. Party Identification object

Explanation:
The Party Identification object is the correct object to use in identity resolution to perform exact match rules on the Loyalty ID. The Party Identification object is a child object of the Individual object that stores different types of identifiers for an individual, such as email, phone, loyalty ID, social media handle, etc. Each identifier has a type, a value, and a source. The consultant can use the Party Identification object to create a match rule that compares the Loyalty ID type and value across different sources and links the corresponding individuals. The other options are not correct objects to use in identity resolution to perform exact match rules on the Loyalty ID. The Loyalty Identification object does not exist in Data Cloud. The Individual object is the parent object that represents a unified profile of an individual, but it does not store the Loyalty ID directly. The Contact Identification object is a child object of the Contact object that stores identifiers for a contact, such as email, phone, etc., but it does not store the Loyalty ID.
References:
Data Modeling Requirements for Identity Resolution Identity Resolution in a Data Space
Configure Identity Resolution Rulesets Map Required Objects
Data and Identity in Data Cloud

Question # 3
A client wants to bring in loyalty data from a custom object in Salesforce CRM that contains a point balance for accrued hotel points and airline points within the same record. The client wants to split these point systems into two separate records for better tracking and processing. What should a consultant recommend in this scenario?
A. Clone the data source object.
B. Use batch transforms to create a second data lake object.
C. Create a junction object in Salesforce CRM and modify the ingestion strategy.
D. Create a data kit from the data lake object and deploy it to the same Data Cloud org.


B. Use batch transforms to create a second data lake object.




Explanation:

Batch transforms are a feature that allows creating new data lake objects based on existing data lake objects and applying transformations on them. This can be useful for splitting, merging, or reshaping data to fit the data model or business requirements. In this case, the consultant can use batch transforms to create a second data lake object that contains only the airline points from the original loyalty data object. The original object can be modified to contain only the hotel points. This way, the client can have two separate records for each point system and track and process them accordingly.

Question # 4
A consultant wants to ensure that every segment managed by multiple brand teams adheres to the same set of exclusion criteria, that are updated on a monthly basis. What is the most efficient option to allow for this capability?
A. Create, publish, and deploy a data kit.
B. Create a reusable container block with common criteria.
C. Create a nested segment.
D. Create a segment and copy it for each brand.


B. Create a reusable container block with common criteria.

Explanation: The most efficient option to allow for this capability is to create a reusable container block with common criteria. A container block is a segment component that can be reused across multiple segments. A container block can contain any combination of filters, nested segments, and exclusion criteria. A consultant can create a container block with the exclusion criteria that apply to all the segments managed by multiple brand teams, and then add the container block to each segment. This way, the consultant can update the exclusion criteria in one place and have them reflected in all the segments that use the container block. 
The other options are not the most efficient options to allow for this capability. Creating, publishing, and deploying a data kit is a way to share data and segments across different data spaces, but it does not allow for updating the exclusion criteria on a monthly basis. Creating a nested segment is a way to combine segments using logical operators, but it does not allow for excluding individuals based on specific criteria. Creating a segment and copying it for each brand is a way to create multiple segments with the same exclusion criteria, but it does not allow for updating the exclusion criteria in one place. 
References: Create a Container Block 
Create a Segment in Data Cloud Create and Publish a Data Kit Create a Nested Segment

Question # 5
Where is value suggestion for attributes in segmentation enabled when creating the DMO?
A. Data Mapping
B. Data Transformation
C. Segment Setup
D. Data Stream Setup


C. Segment Setup


Question # 6
Northern Trail Outfitters (NTD) creates a calculated insight to compute recency, frequency, monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores that it activates to a Marketing Cloud activation target. Which two actions are required when configuring the activation? Choose 2 answers
A. Add additional attributes.
B. Choose a segment.
C. Select contact points.
D. Add the calculated insight in the activation.


B. Choose a segment.
C. Select contact points.

Explanation: To configure an activation to a Marketing Cloud activation target, you need to choose a segment and select contact points. Choosing a segment allows you to specify which unified individuals you want to activate. Selecting contact points allows you to map the attributes from the segment to the fields in the Marketing Cloud data extension. You do not need to add additional attributes or add the calculated insight in the activation, as these are already part of the segment definition. 
References: Create a Marketing Cloud Activation Target; Types of Data Targets in Data Cloud

Question # 7
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 plansz 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?
A. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to the Contact Point Phone data map object from the Contact data stream.
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.
C. Ingest the Contact object and then create a calculated insight to normalize the phone numbers, and then map to the Contact Point Phone data map object.
D. Ingest the Contact object and create formula fields in the Contact data stream on the phone numbers, and then map to the Contact Point Phone data map object.


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.




Explanation:

The most efficient approach is B: Ingest the Contact object and use streaming transforms to normalize phone numbers into a separate Phone DLO, which stores each phone number type (work, home, mobile) in three rows. This data is then mapped to the Contact Point Phone object, ensuring all phone numbers are available for activation (e.g., SMS, calls). Streaming transforms allow real-time normalization (removing spaces, dashes, adding country codes) during ingestion without extra processing or storage.

Question # 8
A retail customer wants to bring customer data from different sources and wants to take advantage of identity resolution so that it can be used in segmentation. On which entity should this be segmented for activation membership?
A. Subscriber
B. Unified Individual
C. Unified Contact
D. Individual


B. Unified Individual

Explanation: The correct answer is B, Unified Individual. A Unified Individual is a record that represents a customer across different data sources, created by applying identity resolution rulesets. Identity resolution rulesets are sets of match and reconciliation rules that define how to link and merge data from different sources based on common attributes. Data Cloud uses identity resolution rulesets to resolve data across multiple data sources and helps you create one record for each customer, regardless of where the data came from1. A retail customer who wants to bring customer data from different sources and use identity resolution for segmentation should segment on the Unified Individual entity, which contains the resolved and consolidated customer data. The other options are incorrect because they do not represent the resolved customer data across different sources. A Subscriber is a record that represents a customer who has opted in to receive marketing communications. A Unified Contact is a record that represents a customer who has a relationship with a specific business unit. An Individual is a record that represents a customer’s profile data from a single data source. 
References: Identity Resolution Ruleset Processing Results Consider Data Implications for Segmentation Prepare for your Salesforce Data Cloud Consultant Credential AI-based Identity Resolution: Linking Diverse Customer Data

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Salesforce Data-Cloud-Consultant Exam Dumps

Exam Name: Salesforce Certified Data Cloud Consultant (SU24)
Certification Name: Salesforce Data Cloud

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