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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:
Topic
Details
Topic 1
- Segmentation and Insights: This topic defines basic concepts of segmentation and use cases, identifies scenarios for analyzing segment membership, configuring, refining, and maintaining segments within Data Cloud, and differentiating between calculated and streaming insights.
Topic 2
- Identity Resolution: It describes matching and how its rule sets are applied. Furthermore, it discusses reconciling data and its rule sets, the results of identity resolution, and use cases.
Topic 3
- Data Cloud Setup and Administration: This topic includes applying Data Cloud permissions, permission sets, org-wide settings. It describes and configures data stream types, and data bundles. Moreover, it discusses use cases for data spaces, creating data spaces, managing and administering Data Cloud using reports, dashboards, flows, packaging, data kits, diagnosing and exploring data using Data Explorer, Profile Explorer, and APIs.
Salesforce Certified Data Cloud Consultant Sample Questions (Q122-Q127):
NEW QUESTION # 122
A consultant is building a segment to announce a new product launch for customers that have previously purchased black pants.
How should the consultant place attributes for product color and product type from the Order Product object to meet this criteria?
- A. Place an attribute for the "black" calculated insight to dynamically apply
- B. Place the attributes for product color and product type in a single container.
- C. Place the attributes for product and product type as direct attributes.
- D. Place the attribute for product color in one container and the attribute for product type in another container.
Answer: B
Explanation:
To create a segment based on the product color and product type from the Order Product object, the consultant should place the attributes for product color and product type in a single container. This way, the segment will include only the customers who have purchased black pants, and not those who have purchased black shirts or blue pants. A container is a grouping of attributes that defines a segment of individuals based on a logical AND operation. Placing the attributes in separate containers would result in a segment that includes customers who have purchased any black product or any pants product, which is not the desired criteria. Placing an attribute for the "black" calculated insight would not work, because calculated insights are based on aggregated data and not individual-level data. Placing the attributes as direct attributes would not work, because direct attributes are used to filter individuals based on their profile data, not their order data. References:
* Create a Segment in Data Cloud
* Learn About Segmentation Tools
* Salesforce Launches: Data Cloud Consultant Certification
NEW QUESTION # 123
Northern Trail Outfitters asks its consultant to extract the runner profiles and activity logs from its Track My Run mobile app and load them into Data Cloud. The marketing department also indicates that they need the last 90 days of historical data and want all new and updated data as it becomes available on a go-forward basis.
As best practice, which sequence of actions should the consultant use to implement this request?
- A. Use streaming ingestion to first load the last 90 days of data, and then use bulk Ingestion to synchronize future data as It becomes available.
- B. Use bulk ingestion to first load the last 90 days of data, and also subsequently use bulk ingestion to synchronize the future data as It becomes available.
- C. Use streaming ingestion to first load the last 90 days of data, and also subsequently use streaming ingestion synchronize future data as It becomes available.
- D. Use bulk ingestion to first load the last 90 days of data, and then use streaming ingestion to synchronize future data as It becomes available.
Answer: D
Explanation:
Initial Data Load: For loading large volumes of historical data, such as the last 90 days of runner profiles and activity logs, bulk ingestion is the most efficient method. It allows for high-throughput data transfer.
* Bulk Ingestion: Use Salesforce Data Cloud's bulk ingestion tools to load the historical data quickly and efficiently.
Ongoing Data Synchronization: To keep the Data Cloud updated with new and modified records as they become available in the Track My Run mobile app, streaming ingestion is appropriate. It ensures near-real-time data updates.
* Streaming Ingestion: Configure streaming ingestion to continuously update the Data Cloud with new and updated data from the mobile app.
Sequence of Actions:
* Step 1: Perform bulk ingestion to import the last 90 days of historical data into Data Cloud.
* Step 2: Set up streaming ingestion to handle ongoing updates and new data as it becomes available.
Best Practice: This approach ensures that the initial large data load is handled efficiently, and ongoing updates are processed in near real-time, providing the marketing department with the most up-to-date data.
References:
* Salesforce Data Cloud Ingestion Methods
* Salesforce Bulk Data Ingestion
* Salesforce Streaming Data Ingestion
NEW QUESTION # 124
A customer has a Master Customer table from their CRM to ingest into Data Cloud. The table contains a name and primary email address, along with other personally Identifiable information (Pll).
How should the fields be mapped to support identity resolution?
- A. Map all fields to the Individual object, adding a custom field for the email address.
- B. Map name to the Individual object and email address to the Contact Phone Email object.
- C. Create a new custom object with fields that directly match the incoming table.
- D. Map all fields to the Customer object.
Answer: B
Explanation:
To support identity resolution in Data Cloud, the fields from the Master Customer table should be mapped to the standard data model objects that are designed for this purpose. The Individual object is used to store the name and other personally identifiable information (PII) of a customer, while the Contact Phone Email object is used to store the primary email address and other contact information of a customer. These objects are linked by a relationship field that indicates the contact information belongs to the individual. By mapping the fields to these objects, Data Cloud can use the identity resolution rules to match and reconcile the profiles from different sources based on the name and email address fields. The other options are not recommended because they either create a new custom object that is not part of the standard data model, or map all fields to the Customer object that is not intended for identity resolution, or map all fields to the Individual object that does not have a standard email address field. References: Data Modeling Requirements for Identity Resolution, Create Unified Individual Profiles
NEW QUESTION # 125
Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use.
Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?
- A. Create new segments by cloning High Investment Balance Customers.
- B. Package High Investment Balance Customers in a data kit.
- C. Create a High Investment Balance calculated insight.
- D. Create new segments using nested segments.
Answer: D
Explanation:
Explanation
Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:
* B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.
* C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.
* D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy. References: Create a Nested Segment - Salesforce, Save Time with Nested Segments (Generally Available) - Salesforce, Calculated Insights - Salesforce, Create and Publish a Data Kit Unit | Salesforce Trailhead, Create a Segment in Data Cloud - Salesforce
NEW QUESTION # 126
Which two dependencies need to be removed prior to disconnecting a data source?
Choose 2 answers
- A. Activation target
- B. Activation
- C. Segment
- D. Data stream
Answer: C,D
Explanation:
* Dependencies in Data Cloud:
Before disconnecting a data source, all dependencies must be removed to prevent data integrity issues.
Reference:
* Identifying Dependencies:
Segment: Segments using data from the source must be deleted or reassigned.
Data Stream: The data stream must be disconnected, as it directly relies on the data source.
* Steps to Remove Dependencies:
Remove Segments:
Navigate to the Segmentation interface in Salesforce Data Cloud.
Identify and delete segments relying on the data source.
Disconnect Data Stream:
Go to the Data Stream settings.
Locate and disconnect the data stream associated with the source.
* Practical Application:
Example: When preparing to disconnect a legacy CRM system, ensure all segments and data streams using its data are properly removed or migrated.
NEW QUESTION # 127
......
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