A consultant is setting up a data stream with transactional data, Which field type should the consultant choose to ensure that leading zeros in the purchase order number are preserved?

1. A. Text
2. B. Number
3. C. Decimal
4. D. Serial

Correct Answer: A
The field type Text should be chosen to ensure that leading zeros in the purchase order number are preserved. This is because text fields store alphanumeric characters as strings, and do not remove any leading or trailing characters. On the other hand, number, decimal, and serial fields store numeric values as numbers, and automatically remove any leading zeros when displaying or exporting the data123. Therefore, text fields are more suitable for storing data that needs to retain its original format, such as purchase order numbers, zip codes, phone numbers, etc. References:
✑ Zeros at the start of a field appear to be omitted in Data Exports
✑ Keep First ‘0’ When Importing a CSV File
✑ Import and export address fields that begin with a zero or contain a plus symbol

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?

1. A. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to theContact Point Phone data map object from the Contact data stream.
2. B. Ingest the Contact object and use streaming transforms to normalize the phone numbers fromthe 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.
3. 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.
4. D. Ingest the Contact object and create formula fields in the Contact data stream on the phonenumbers, and then map to the Contact Point Phone data map object.

Correct Answer: B
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.
The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streaming transforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the
existing fields in the Contact object. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone

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?

1. A. Data streams for each brand
2. B. Data model objects for each brand
3. C. Data spaces for each brand
4. D. Data sources for each brand

Correct Answer: C
Data spaces are logical containers that allow you to separate and organize your data by different criteria, such as brand, region, product, or business unit1. Data spaces can help you manage data access, security, and governance, as well as enable cross-cloud data integration and activation2. For NTO, data spaces can support their desire to separate their data by brand, so that they can have different data models, rules, and insights for their outdoor lifestyle clothing and gourmet camping food businesses. Data spaces can also help NTO comply with any data privacy and security regulations that may apply to their different brands3. The other options are incorrect because they do not provide the same level of data separation and organization as data spaces. Data streams are used to ingest data from different sources into Data Cloud, but they do not separate the data by brand4. Data model objects are used to define the structure and attributes of the data, but they do not isolate the data by brand5. Data sources are used to identify the origin and type of the data, but they do not partition the data by brand. References: Data
Spaces Overview, Create Data Spaces, Data Privacy and Security in Data Cloud, Data Streams Overview, Data Model Objects Overview, [Data Sources Overview]

Cloud Kicks received a Request to be Forgotten by a customer.
In which two ways should a consultant use Data Cloud to honor this request? Choose 2 answers

1. A. Delete the data from the incoming data stream and perform a full refresh.
2. B. Add the Individual ID to a headerless file and use the delete from file functionality.
3. C. Use Data Explorer to locate and manually remove the Individual.
4. D. Use the Consent API to suppress processing and delete the Individual and related records fromsource data streams.

Correct Answer: BD
To honor a Request to be Forgotten by a customer, a consultant should use Data Cloud in two ways:
✑ Add the Individual ID to a headerless file and use the delete from file functionality. This option allows the consultant to delete multiple Individuals from Data Cloud by uploading a CSV file with their IDs1. The deletion process is asynchronous and can take up to 24 hours to complete1.
✑ Use the Consent API to suppress processing and delete the Individual and related records from source data streams. This option allows the consultant to submit a Data Deletion request for an Individual profile in Data Cloud using the Consent API2. A Data Deletion request deletes the specified Individual entity and any entities where a relationship has been defined between that entity’s identifying attribute and the Individual ID attribute2. The deletion process is reprocessed at 30, 60, and 90 days to ensure a full deletion2. The other options are not correct because:
✑ Deleting the data from the incoming data stream and performing a full refresh will not delete the existing data in Data Cloud, only the new data from the source system3.
✑ Using Data Explorer to locate and manually remove the Individual will not delete the related records from the source data streams, only the Individual entity in Data Cloud. References:
✑ Delete Individuals from Data Cloud
✑ Requesting Data Deletion or Right to Be Forgotten
✑ Data Refresh for Data Cloud
✑ [Data Explorer]

A user is not seeing suggested values from newly-modeled data when building a segment. What is causing this issue?

1. A. Value suggestion is still processing and to be available.
2. B. Value suggestion requires Data Aware Specialist permissions at a minimum.
3. C. Value suggestion can only work on direct attributes and not related attributes.
4. D. Value suggestion will only return result for the first 50 values of a specific attribute.

Correct Answer: A
Value suggestion is a feature that allows users to see suggested values for data model object (DMO) fields when creating segment filters. However, this feature can take up to 24 hours to process and display the values for newly-modeled data. Therefore, if a user is not seeing suggested values from newly-modeled data, it is likely that the value suggestion is still processing and will be available soon. The other options are incorrect because value suggestion does not require any specific permissions, can work on both direct and related attributes, and can return more than 50 values for a specific attribute, depending on the data type and frequency of the values. References: Use Value Suggestions in Segmentation, Data Cloud Limits and Guidelines