Salesforce-Data-Cloud Questions and Answers

These two remedies can help resolve the error “Segment references too many Data Lake Objects (DLOs)”. Spacing out the segment schedules can reduce the concurrent load on the Data Lake Objects and improve performance. Splitting the segment into smaller segments can reduce the number of Data Lake Objects that are referenced by each segment. References: https://help.salesforce.com/s/articleView ?

Only one metric can be present in one segment container. A segment container is a logical grouping of segments that share the same metric and granularity. A metric is a calculated insight that defines and calculates a multidimensional measure on your data. A granularity is the level of detail at which you want to analyze your data, such as daily, weekly, or monthly. You can create multiple segment containers with different metrics and granularities, but each segment container can only have one metric. References: Segment Containers

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

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

To pause a segment activation but with the intent of using that segment again, the administrator should stop the publish schedule for that segment. This will stop the segment from being refreshed and activated, but it will not delete or inactivate the segment. The administrator can resume the publish schedule at any time.

Calculated Insights are predefined and calculated metrics that can help you build segments. Some of the benefits of Calculated Insights over Segmentation Operators are:

• Calculated Insights are better suited for non-trivial calculations, such as calculating a Net Promoter Score as a percentage, which would require multiple steps and operators in segmentation.
• Calculated Insights can query engagement data greater than 2 years, while segmentation operators are limited to 2 years of engagement data.
• Calculated Insights are better suited for complex queries over multiple objects, such as joining data from different sources or aggregating data across different levels.

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]

A window function is a unique requirement of a Streaming Insight query. It defines the time interval for the query to run on the streaming data and the frequency of the query execution. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_streaming_insights.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/

These are the two dependencies that need to be removed prior to disconnecting a data source. A data stream is a process that ingests data from a data source into Data Cloud. A segment is a group of customers that meet certain criteria based on their data. If a data source is disconnected, any data streams or segments that depend on it will no longer function properly. References: [Data Sources], [Data Streams], [Segments]

The Text field type should be chosen to preserve leading zeros in the purchase order number, as this field type stores alphanumeric characters as strings. The Number and Decimal field types store numeric values as numbers, which would remove any leading zeros. The Serial field type is not a valid field type in Data Cloud. References: [Field Types]

According to the Data Cloud documentation, segments can only be created on the Unified Individual entity, which represents the result of Identity Resolution. The other data model object categories, such as Profile, Engagement, and Other, are not available for segmentation.

According to the Data Cloud documentation, the Is Equal To operator is case-insensitive, meaning it matches values regardless of capitalization. Therefore, the segmentation criteria filtering on City | Is Equal To | ‘San Jose’ will include cities that contain either ‘San Jose’ or ‘san jose’.

The Ignore Empty Value option is a setting for reconciliation rules, which determine the logic for data selection in a unified profile7. If this option is enabled, empty fields are ignored when applying the reconciliation rule. For example, if the rule is to select the most frequent value, and one of the values is empty, it will not be counted as a frequency.

This action should be performed to accommodate the request to delete personal data in Data Cloud. The Consent API allows you to set a consent flag for an individual that prevents further processing of their data and deletes their records from source data streams. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_consent_api.htm &type=5

One possible reason why a new custom field is not available in the data stream is that the Salesforce Integration User, which is used to connect to the CRM source, does not have “Read” permissions on that field2. To fix this issue, the administrator needs to grant the appropriate permissions to the Integration User profile or permission set.

This method should be used when performing aggregations in windows of 15 minutes on data collected via the Interactions SDK and Mobile SDK. Streaming Insight allows you to query and aggregate data from real-time streams using window functions. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_streaming_insights.htm &type=5

Value suggestion is a feature of Data Cloud that allows you to identify and select text attributes from a picklist of options. You can use value suggestion to standardize values across different data sources and improve data quality. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_value_suggestion.htm &type=5

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]

You can modify the attribute names in the data model by editing the field label or API name of the corresponding field. This will change how the attribute name appears in the Cloud File Storage target. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_data_model.htm &type=5

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

To track the daily transaction volume for each customer in real time and send out a notification as soon as it detects volume outside a customer’s normal range, the administrator should use a Streaming Insight paired with a Data Action. A Streaming Insight is a metric that is calculated on streaming data as it is ingested into Data Cloud, allowing near-real-time analysis of customer behavior. A Data Action is an action that is triggered by a Streaming Insight, such as sending an email, updating a record, or calling an API. By using these features, the administrator can monitor and respond to customer transactions in real time.