Salesforce Salesforce-Data-Cloud Dumps

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.

You can customize the Data Cloud canonical model by adding, editing, or deleting objects, fields, and relationships. You can also modify the properties, labels, and descriptions of these components. References: &type=5

The maximum limit of the number of records for Full Refresh Extract Method is 10 million. Full Refresh Extract Method is an option for extracting data from Marketing Cloud to Data Cloud. It extracts all records from a data extension regardless of when they were created or modified. If the number of records exceeds 10 million, an error occurs and no records are extracted. References: Extract Methods

These two benefits Data Cloud provides a company in relation to managing customer data. Unified Identity Resolution allows a company to link data from different sources and create a single view of each customer. Data Harmonization allows a company to standardize and enrich data from different sources and improve data quality and usability. References: &type=5 &type=5

The Email field from a CRM Contact object should be mapped to the Contact Point Email entity in the Customer 360 data model. This entity represents an email address that is associated with an individual or an account contact. References: [Contact Point Email Entity]

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

According to the Data Cloud documentation, when using two separate containers linked by an AND operator, the segment includes individuals who meet both conditions. In this case, the segment includes individuals who purchased at least one product with the color attribute equal to ‘red’, and also purchased at least one product with the primary product category attribute equal to ‘shoes’. The products do not have to be the same or in the same order line item.

This operator can be used to create a segment for a birthday campaign that is evaluated daily. It compares a date attribute to the current date and returns true if they have the same month and day, regardless of the year. References: &type=5

The data types can be changed during ingestion before the Data Source Object (DSO) is created. A DSO is a container for ingested data that defines the schema, format, and metadata of the data. Data types can be changed in the Data Explorer before creating a DSO by using the Edit Schema option. Once a DSO is created, the data types cannot be changed. References: Create a Data Source Object

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 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]

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

The transaction data stream category should be assigned to use the data for time-based operations in segmentation and calculated insights. This category indicates that the data stream contains events that occur at a specific point in time and have a duration. References: &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:

To create a Cloud File Storage activation target, you need to provide access and secret keys for authentication5. These keys are generated by your cloud storage provider, such as Amazon S3 or Google Cloud Storage.

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: &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 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: &type=5

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

You can add new measures or dimensions to a Calculated Insight by selecting them from the available attributes list

This configuration will ensure that Data Cloud only ingests new files from the Amazon S3 bucket and updates or inserts new records into the data stream without deleting existing records. References: &type=5

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: &type=5 &type=5 &type=5

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: &type=5

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.

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

This is the first thing a business stakeholder should focus on when considering a Data Cloud implementation. Obtaining cross-organizational buy-in involves aligning the goals and expectations of different teams and stakeholders who will use or contribute to Data Cloud. References:

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]