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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Data Cloud Consultant Exam |
| Exam Number: | Data-Con-101 |
| Real Exam Qty: | 60–65 (including up to 5 unscored items) |
| Exam Price: | USD 200 (+ applicable taxes) |
| Passing Score: | 62% |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 105 minutes |
| Related Certifications: | Salesforce Certified Consultant |
| Available Languages: | English |
| Exam Format: | Multiple-select, Multiple-choice |
| Recommended Training: | Cert Prep: Salesforce Certified Data Cloud Consultant Trailhead: Data Cloud Fundamentals (SDC101) |
| Exam Registration: | Pearson VUE Kryterion Webassessor |
| Sample Questions: | Salesforce Data-Con-101 Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No mandatory prerequisites; recommended 2+ years experience in data strategy, modeling, and Salesforce implementation |
| Official Syllabus URL: | https://trailheadacademy.salesforce.com/certificate/exam-data-cloud---Data-Con-101 |
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NEW QUESTION # 117
How does Data Cloud handle an individual's Right to be Forgotten?
Answer: A
Explanation:
Data Cloud handles an individual's Right to be Forgotten by deleting the specified Individual and records from any data model object/data lake object related to the Individual. This means that Data Cloud removes all the data associated with the individual from the data space, including the data from the source objects, the unified individual profile, and any related objects. Data Cloud also deletes the Unified Individual Link record that links the individual to the source records. Data Cloud uses the Consent API to process the Right to be Forgotten requests, which are reprocessed at 30, 60, and 90 days to ensure a full deletion.
The other options are not correct descriptions of how Data Cloud handles an individual's Right to be Forgotten. Data Cloud does not delete the records from all data source objects, as this would affect the data integrity and availability of the source systems. Data Cloud also does not delete only the specified Individual record and its Unified Individual Link record, as this would leave the source records and the related records intact. Data Cloud also does not delete only the specified Individual and records from any data source object mapped to the Individual data model object, as this would leave the related records intact.
Requesting Data Deletion or Right to Be Forgotten
Data Deletion for Data Cloud
Use the Consent API with Data Cloud
Data and Identity in Data Cloud
NEW QUESTION # 118
A user wants to be able to create a multi-dimensional metric to identify unified individual lifetime value (LTV).
Which sequence of data model object (DMO) joins is necessary within the calculated Insight to enable this calculation?
Answer: D
Explanation:
To create a multi-dimensional metric to identify unified individual lifetime value (LTV), the sequence of data model object (DMO) joins that is necessary within the calculated Insight is Unified Individual > Unified Link Individual > Sales Order. This is because the Unified Individual DMO represents the unified profile of an individual or entity that is created by identity resolution1. The Unified Link Individual DMO represents the link between a unified individual and an individual from a source system2. The Sales Order DMO represents the sales order information from a source system3. By joining these three DMOs, you can calculate the LTV of a unified individual based on the sales order data from different source systems. The other options are incorrect because they do not join the correct DMOs to enable the LTV calculation. Option B is incorrect because the Individual DMO represents the source profile of an individual or entity from a source system, not the unified profile4. Option C is incorrect because the join order is reversed, and you need to start with the Unified Individual DMO to identify the unified profile. Option D is incorrect because it is missing the Unified Link Individual DMO, which is needed to link the unified profile with the source profile. References: Unified Individual Data Model Object, Unified Link Individual Data Model Object, Sales Order Data Model Object, Individual Data Model Object
NEW QUESTION # 119
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
Answer: B,D
Explanation:
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]
NEW QUESTION # 120
A company wants to test its marketing campaigns with different target populations.
What should the consultant adjust in the Segment Canvas interface to get different populations?
Answer: D
Explanation:
Segmentation in Salesforce Data Cloud:
The Segment Canvas interface is used to define and adjust target populations for marketing campaigns.
Reference: Salesforce Segment Canvas Documentation
Elements for Adjusting Target Populations:
Direct Attributes: These are specific attributes directly related to the target entity (e.g., customer age, location).
Related Attributes: These are attributes related to other entities connected to the target entity (e.g., purchase history).
Population Filters: Filters applied to define and narrow down the segment population (e.g., active customers).
Reference: Salesforce Segmentation Guide
Steps to Adjust Populations in Segment Canvas:
Direct Attributes: Select attributes that directly describe the target population.
Related Attributes: Incorporate attributes from related entities to enrich the segment criteria.
Population Filters: Apply filters to refine and target specific subsets of the population.
Example: To create a segment of "Active Customers Aged 25-35," use age as a direct attribute, purchase activity as a related attribute, and apply population filters for activity status and age range.
Reference: Salesforce Segment Canvas Tutorial
Practical Application:
Navigate to the Segment Canvas.
Adjust direct attributes and related attributes based on campaign goals.
Apply population filters to fine-tune the target audience.
Reference: Salesforce Marketing Cloud Segmentation Best Practices
NEW QUESTION # 121
A consultant is troubleshooting a segment error.
Which error message is solved by using calculated insights Instead of nested segments?
Answer: D
Explanation:
Segment Errors in Data Cloud: Segments in Salesforce Data Cloud can encounter errors due to various reasons, including complexity and nested segments.
Calculated Insights vs. Nested Segments:
Complex Segments: If a segment is too complex due to extensive nesting or numerous conditions, it can lead to errors.
Simplification with Calculated Insights: Using calculated insights can simplify segment creation by pre- computing and storing complex logic or aggregations, which can then be referenced directly in the segment.
Solution:
Step 1: Identify the segment causing the "Segment is too complex" error.
Step 2: Break down complex logic into calculated insights.
Step 3: Use these calculated insights in segment definitions to reduce complexity.
References:
Salesforce Data Cloud Calculated Insights
Salesforce Data Cloud Segment Creation
NEW QUESTION # 122
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