Question # 1
| During an implementation project, a consultant completed ingestion of all data streams for their customer.
Prior to segmenting and acting on that data, which additional configuration is required? | | A. Data Activation | | B. Calculated Insights | | C. Data Mapping | | D. Identity Resolution |
D. Identity Resolution
Explanation:
After ingesting data from different sources into Data Cloud, the additional configuration that is required before segmenting and acting on that data is Identity Resolution. Identity Resolution is the process of matching and reconciling source profiles from different data sources and creating unified profiles that represent a single individual or entity1. Identity Resolution enables you to create a 360-degree view of your customers and prospects, and to segment and activate them based on their attributes and behaviors2. To configure Identity Resolution, you need to create and deploy a ruleset that defines the match rules and reconciliation rules for your data3. The other options are incorrect because they are not required before segmenting and acting on the data. Data Activation is the process of sending data from Data Cloud to other Salesforce clouds or external destinations for marketing, sales, or service purposes4. Calculated Insights are derived attributes that are computed based on the source or unified data, such as lifetime value, churn risk, or product affinity5. Data Mapping is the process of mapping source attributes to unified attributes in the data model. These configurations can be done after segmenting and acting on the data, or in parallel with Identity Resolution, but they are not prerequisites for it.
References:
Identity Resolution Overview, Segment and Activate Data in Data Cloud, Configure Identity Resolution Rulesets, Data Activation Overview, Calculated Insights Overview, [Data Mapping Overview]
Question # 2
| A Data Cloud consultant is working with data that is clean and organized. However, the various schemas refer to a person by multiple names — such as user; contact, and subscriber — and need a standard mapping.
Which term describes the process of mapping these different schema points into a standard data model? | | A. Segment | | B. Harmonize | | C. Unify | | D. Transform |
B. Harmonize
Explanation: Introduction to Data Harmonization: Data harmonization is the process of bringing together data from different sources and making it consistent. Reference: Salesforce Data Harmonization Overview
Mapping Different Schema Points: In Data Cloud, different schemas may refer to the same entity using different names (e.g., user, contact, subscriber). Harmonization involves standardizing these different terms into a single, consistent schema. Reference: Salesforce Schema Mapping Guide
Process of Harmonization:
Identify Variations: Recognize the different names and fields referring to the same entity across schemas. Standard Mapping: Create a standard data model and map the various schema points to this model. Example: Mapping “user”, “contact”, and “subscriber” to a single standard entity like “Customer.” Reference: Salesforce Data Model Harmonization Documentation
Steps to Harmonize Data: Define a standard data model. Map the fields from different schemas to this standard model. Ensure consistency across the data ecosystem. Reference: Salesforce Data Harmonization Best Practices
Question # 3
| Cumulus Financial is experiencing delays in publishing multiple segments simultaneously.
The company wants to avoid reducing the
frequency at which segments are published, while retaining the same segments in place
today.
Which action should a consultant take to alleviate this issue? | | A. Enable rapid segment publishing to all to segment to reduce generation time. | | B. Reduce the number of segments being published | | C. Increase the Data Cloud segmentation concurrency limit. | | D. Adjust the publish schedule start time of each segment to prevent overlapping
processes. |
D. Adjust the publish schedule start time of each segment to prevent overlapping
processes.
Question # 4
| A customer requests that their personal data be deleted. Which action should the consultant take to accommodate this request in Data Cloud? | | A. Use a streaming API call to delete the customer's information. | | B. Use Profile Explorer to delete the customer data from Data Cloud. | | C. Use Consent API to request deletion of the customer's information. | | D. Use the Data Rights Subject Request tool to request deletion of the customer's information. |
C. Use Consent API to request deletion of the customer's information.
Explanation:
The Data Rights Subject Request tool is a feature that allows Data Cloud users to manage customer requests for data access, deletion, or portability. The tool provides a user interface and an API to create, track, and fulfill data rights requests. The tool also generates a report that contains the customer’s personal data and the actions taken to comply with the request. The consultant should use this tool to accommodate the customer’s request for data deletion in Data Cloud. References: Data Rights Subject Request Tool, Create a Data Rights Subject Request.
Question # 5
| 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 # 6
| Northern Trail Outfitters (NTO), an outdoor lifestyle clothing brand, recently started a new line of business. The new business specializes in gourmet camping food. For business reasons as well as security reasons, it's important to NTO to keep all Data Cloud data separated by brand. Which capability best supports NTO's desire to separate its data by brand? | | A. Data sources for each brand | | B. Data model objects for each brand | | C. Data spaces for each brand | | D. Data streams for each brand |
C. Data spaces for each brand
Question # 7
| A customer has a custom Customer Email c object related to the standard Contact object in Salesforce CRM. This custom object stores the email address a Contact that they want to use for activation. To which data entity is mapped? | | A. Contact | | B. Contact Point_Email | | C. Custom customer Email c object | | D. Individual |
B. Contact Point_Email
Explanation:
The Contact Point_Email object is the data entity that represents an email address associated with an individual in Data Cloud. It is part of the Customer 360 Data Model, which is a standardized data model that defines common entities and relationships for customer data. The Contact Point_Email object can be mapped to any custom or standard object that stores email addresses in Salesforce CRM, such as the custom Customer Email c object. The other options are not the correct data entities to map to because:
A. The Contact object is the data entity that represents a person who is associated with an account that is a customer, partner, or competitor in Salesforce CRM. It is not the data entity that represents an email address in Data Cloud.
C. The custom Customer Email c object is not a data entity in Data Cloud, but a custom object in Salesforce CRM. It can be mapped to a data entity in Data Cloud, such as the Contact Point_Email object, but it is not a data entity itself.
D. The Individual object is the data entity that represents a unique person in Data Cloud. It is the core entity for managing consent and privacy preferences, and it can be related to one or more contact points, such as email addresses, phone numbers, or social media handles. It is not the data entity that represents an email address in Data Cloud.
References:
Customer 360 Data Model: Individual and Contact Points - Salesforce, Contact Point_Email | Object Reference for the Salesforce Platform | Salesforce Developers, [Contact | Object Reference for the Salesforce Platform | Salesforce Developers], [Individual | Object Reference for the Salesforce Platform | Salesforce Developers]
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FAQs of Data-Cloud-Consultant Exam
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