Salesforce-Data-Cloud Salesforce Data CloudAccredited Professional Exam (SU24) Questions and Answers

These three features can be used to validate the data in the unified profile object. Data Explorer allows you to view the ingested data from different sources and how it is mapped to the unified profile object. Query API allows you to query the unified profile object using SOQL or SQL queries. Profile Explorer allows you to view the unified profile records and their attributes. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_explorer.htm &type=5 https://help.salesforce.com/s/articleView?id=sf.c360_a_query_api.htm &type=5 https://help.salesforce.com/s/articleView?id=sf.c360_a_profile_explorer.htm &type=5

This is what it means to build a trust-based, first-party data asset. It involves respecting the privacy and preferences of individuals who share their data with an organization and ensuring that the data is used in a responsible and ethical way. References: https://www.salesforce.com/products/genie/overview/

To create a multi-dimensional metric to identify unified individual lifetime value (LTV), the administrator needs to join the following data model objects (DMOs) in the Calculated Insight:

• Unified Individual: This DMO represents the unified profile of an individual, which contains attributes from multiple sources.
• Unified Link Individual: This DMO represents the link between an Individual DMO and a Unified Individual DMO.
• Sales Order: This DMO represents a transaction or purchase made by an individual.

The sequence of joins should start from the Unified Individual DMO, then join the Unified Link Individual DMO using the UnifiedIndividualId field, and then join the Sales Order DMO using the IndividualId field. This way, the administrator can access the sales order data for each unified individual and calculate their lifetime value.

If no file name is specified in AWS S3 data stream during ingestion, the system does not fetch any file and the data stream shows an error. AWS S3 data stream is a feature that allows you to stream data from Amazon Web Services Simple Storage Service (AWS S3) to Data Cloud in near real time. You need to specify the file name or prefix of the files that you want to ingest from your S3 bucket. If you leave this field blank, the system cannot find any matching files and returns an error message. References: AWS S3 Data Stream

This is the first step to set up and configure a Data Cloud instance after it has been provisioned. The customer needs to connect to the Salesforce CRM org that Data Cloud is provisioned in, using the Salesforce CRM connector. This will allow Data Cloud to access the CRM data and map it to the Customer 360 data model. References: [Salesforce CRM Connector], [Data Cloud Setup]

These two statements are true about using consent API and exercising right to be forgotten. Data deletion requests are reprocessed at regular intervals to ensure that any new data related to the individual is deleted. Data deletion requests are submitted for individual profiles that represent unique identities in Data Cloud. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_consent_api.htm &type=5

These three out-of-the-box connectors are available for Data Cloud. They allow you to ingest data from Marketing Cloud, B2C Commerce, or Amazon S3 into Data Cloud and map it to the Data Cloud data model. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_connectors.htm &type=5

The Customer 360 data model is a conceptual model and a canonical model. A conceptual model is a high-level representation of the entities and relationships in a domain of interest, without specifying any implementation details. A canonical model is a common and consistent data model that can be used across different systems and applications. References: [Customer 360 Data Model Overview], [Conceptual Model], [Canonical Model]

Data Cloud’s primary value to customers is that it provides a platform that enables customers to create a unified view of a customer and their related data across different channels and systems, using the Customer 360 data model as the foundation. References: [Data Cloud Overview], [Customer 360 Data Model]

To create segments of users based on their Customer Lifetime Value, the customer needs to first ingest the source data into Data Cloud, then map the data to the Customer 360 data model, then create a calculated insight using the formula editor, and finally use the calculated insight in segmentation. References: [Data Ingestion], [Data Mapping], [Calculated Insights], [Segmentation]

The type of the Event Time Field while ingesting Engagement data should be Immutable. Immutable means that the field value cannot be changed once it is ingested. The Event Time Field is a mandatory field for Engagement data that represents the timestamp of when an event occurred. It is important to keep this field immutable to ensure the accuracy and consistency of the event data. References: Engagement Data Model

Data Cloud can address these two common use cases by providing a platform that allows customers to ingest, map, enrich, and activate data from various sources using the Customer 360 data model, and by enabling customers to create segments, insights, and activations based on their customer data. References: [Data Cloud Overview], [Customer 360 Data Model], [Data Activation]

These two features are impacted by the timezone setting in the org Data Cloud is provisioned in. The segment schedule determines how often a segment is refreshed based on the selected timezone. The activation schedule determines how often a segment is exported to an activation target based on the selected timezone. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_segmentation_schedule.htm &type=5 https://help.salesforce.com/s/articleView?id=sf.c360_a_activation_schedule.htm &type=5

This feature can help Northern Trail Outfitters calculate each customer’s lifetime value (LTV) and create breakdowns of the revenue sourced by different channels. Streaming data transformations allow you to transform and enrich streaming data from different sources using formulas and operators. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_streaming_data_transform.htm &type=5

The Source Sequence reconciliation rule allows you to sort your data sources in order of most to least preferred for inclusion in a unified profile9. For example, you can specify that data from Marketing Cloud should have higher priority than data from Service Cloud when selecting attributes such as first name or last name.

Numeric is the only type of measures that support aggregate functions. Measures are numeric fields that can be aggregated using functions such as SUM, AVG, MIN, MAX, or COUNT. For example, you can use a measure to calculate the total revenue or the average order value from your data. Date, Text, and Boolean are not types of measures, but types of dimensions. Dimensions are fields that can be used to group or filter data, but not to perform calculations. References: Measures and Dimensions

Identity Resolution creates three types of objects8:

• Unified Individual: This object represents the unified profile of an individual, which contains attributes from multiple sources.
• Unified Contact Point: This object represents the unified contact information of an individual, such as email, phone, address, or app.
• Unified Link: This object represents the link between an Individual object and a Unified Individual object, or between a Contact Point object and a Unified Contact Point object.

The data streams that use the Marketing Cloud Connector refresh data using Marketing Cloud’s Automation Studio. The Marketing Cloud Connector is a feature that allows you to stream data from Marketing Cloud to Data Cloud in near real time. The connector uses Automation Studio to schedule and run data extracts from Marketing Cloud data extensions and send them to Data Cloud via SFTP. You can configure the frequency and time of the data extracts in Automation Studio. References: Marketing Cloud Connector

This platform is going to present a challenge in terms of connection limits for Data Cloud. Data Cloud supports up to 3 Marketing Cloud connections per org, which means that only 3 Marketing Cloud accounts can be connected as data sources or activation targets. The other platforms have higher or no limits for connections. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_limits.htm &type=5

This permission setting should be checked if the custom CRM object is not available in New Data Stream configuration. The user who connects the Salesforce CRM connector must have View All permission for the custom object in the source CRM org. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_salesforce_crm.htm &type=5

A data stream cannot be deleted if any of its attributes are mapped to a Data Model object or used in a Calculated Insight3. These dependencies need to be removed before deleting the data stream.

Reducing the number of matching rules can increase the consolidation rate for Identity Resolution, because it reduces the chances of finding multiple matches for the same individual. Matching rules tell Data Cloud which profiles to unify during the identity resolution process. If there are too many matching rules, Data Cloud might find more than one match for a given profile, resulting in a lower consolidation rate. References: Identity Resolution Match Rules

The recommended order of processes for updating data in Data Cloud is as follows6:

• Refresh Data Stream: This process updates the data from the source, such as Amazon S3, into Data Cloud.
• Identity Resolution: This process runs the rulesets that link together multiple records of an individual into a unified profile.
• Calculated Insight: This process calculates additional attributes based on existing data, such as lifetime value or churn risk.

These are the three data categories to select from when setting up the data source object or schema for data ingestion. Engagement data includes information about how customers interact with a brand or product, such as clicks, views, likes, etc. Order data includes information about transactions or purchases made by customers, such as order ID, amount, date, etc. Profile data includes information about customers’ attributes or preferences, such as name, email, gender, etc. References: [Data Categories]