A Data Cloud consultant is in the process of setting up data streams for a new service based data source. When ingesting Case data, which field is recommended to be associated with the Event Time Field?
A. Last Modified Data
B. Creation Date
C. Escalation Date
D. Resolution Date
Cumulus Financial wants to segregate Salesforce CRM Account data based on Country for its Data Cloud users. What should the consultant do to accomplish this?
A. Use Salesforce sharing rules on the Account object to filter and segregate records based on Country.
B. Use formula fields based on the Account Country field to filter incoming records.
C. Use streaming transforms to filter out Account data based on Country and map to separate data model objects accordingly.
D. Use the data spaces feature and apply filtering on the Account data lake object based on Country.
A customer has a calculated insight about lifetime value. What does the consultant need to be aware of if the calculated insight needs to be modified?
A. New dimensions can be added.
B. Existing dimensions can be removed.
C. Existing measures can be removed.
D. New measures can be added.
Which three actions can be applied to a previously created segment?
A. Reactivate
B. Export
C. Delete
D. Copy
E. Inactivate
Explanation:
These three actions can be applied to a previously created segment. You can export a segment to a CSV file, delete a segment from Data Cloud, or copy a segment to create a duplicate segment with the same criteria.
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?
A. Data Cleansing
B. Harmonization
C. Data Consolidation
D. Identity Resolution
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.
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.
To import campaign members into a campaign in CRM a user wants to export the segment to Amazon S3. The resulting file needs to include CRM Campaign ID in the name. How can this outcome be achieved?
A. Include campaign identifier into the activation name
B. Hard-code the campaign identifier as a new attribute in the campaign activation
C. Include campaign identifier into the filename specification
D. Include campaign identifier into the segment name
Explanation:
You can use the filename specification option in the Amazon S3 activation to customize the name of the file that is exported. You can use variables such as {campaignId} to include the CRM campaign ID in the file name.
What does the Ignore Empty Value option do in identity resolution?
A. Ignores empty fields when running any custom match rules
B. Ignores empty fields when running reconciliation rules
C. Ignores Individual object records with empty fields when running identity resolution rules
D. Ignores empty fields when running the standard match rules
Explanation:
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud. In what order should each process be run to ensure that freshly imported data is ready and available to use for any segment?
A. Calculated Insight > Refresh Data Stream > Identity Resolution
B. Refresh Data Stream > Calculated Insight > Identity Resolution
C. Identity Resolution > Refresh Data Stream > Calculated Insight
D. Refresh Data Stream > Identity Resolution > Calculated Insight
Explanation:
To ensure that freshly imported data from an Amazon S3 Bucket is ready and available to use for any segment, the following processes should be run in this order:
Refresh Data Stream: This process updates the data lake objects in Data Cloud with the latest data from the source system. It can be configured to run automatically or manually, depending on the data stream settings. Refreshing the data stream ensures that Data Cloud has the most recent and accurate data from the Amazon S3 Bucket.
Identity Resolution: This process creates unified individual profiles by matching and consolidating source profiles from different data streams based on the identity resolution ruleset. It runs daily by default, but can be triggered manually as well. Identity resolution ensures that Data Cloud has a single view of each customer across different data sources.
Calculated Insight: This process performs calculations on data lake objects or CRM data and returns a result as a new data object. It can be used to create metrics or measures for segmentation or analysis purposes. Calculated insights ensure that Data Cloud has the derived data that can be used for personalization or activation.
A customer is concerned that the consolidation rate displayed in the identity resolution is quite low compared to their initial estimations. Which configuration change should a consultant consider in order to increase the consolidation rate?
A. Change reconciliation rules to Most Occurring.
B. Increase the number of matching rules.
C. Include additional attributes in the existing matching rules.
D. Reduce the number of matching rules.
Explanation:
The consolidation rate is the amount by which source profiles are combined to produce unified profiles, calculated as 1 - (number of unified individuals / number of source individuals). For example, if you ingest 100 source records and create 80 unified profiles, your consolidation rate is 20%. To increase the consolidation rate, you need to increase the number of matches between source profiles, which can be done by adding more match rules. Match rules define the criteria for matching source profiles based on their attributes. By increasing the number of match rules, you can increase the chances of finding matches between source profiles and thus increase the consolidation rate. On the other hand, changing reconciliation rules, including additional attributes, or reducing the number of match rules can decrease the consolidation rate, as they can either reduce the number of matches or increase the number of unified profiles.
A consultant is ingesting a list of employees from their human resources database that they want to segment on. Which data stream category should the consultant choose when ingesting this data?
A. Profile Data
B. Contact Data
C. Other Data
D. Engagement Data
Cumulus Financial uses calculated insights to compute the total banking value per branch for its high net worth customers. In the calculated insight, "banking value" is a metric, "branch" is a dimension, and "high net worth" is a filter. What can be included as an attribute in activation?
A. "high net worth" (filter)
B. "branch" (dimension) and "banking metric)
C. "banking value" (metric)
D. "branch" (dimension)
Explanation:
According to the Salesforce Data Cloud documentation, an attribute is a dimension or a measure that can be used in activation. A dimension is a categorical variable that can be used to group or filter data, such as branch, region, or product. A measure is a numerical variable that can be used to calculate metrics, such as revenue, profit, or count. A filter is a condition that can be applied to limit the data that is used in a calculated insight, such as high net worth, age range, or gender. In this question, the calculated insight uses “banking value” as a metric, which is a measure, and “branch” as a dimension. Therefore, only “branch” can be included as an attribute in activation, since it is a dimension. The other options are either measures or filters, which are not attributes.
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