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Salesforce Salesforce-Data-Cloud Exam Syllabus Topics:

SectionObjectives
Topic 1: Identity Resolution- Describe matching and reconciliation rules
- Explain the purpose and configuration of Identity Resolution
Topic 2: Data Cloud Overview- Explain the purpose and functionality of Data Cloud
- Describe the Data Cloud ecosystem and how it fits into the Salesforce platform
Topic 3: Segmentation and Calculated Insights- Create and manage segments
- Explain calculated insights
Topic 4: Activation- Configure activations to various targets
- Describe activation use cases
Topic 5: Data Cloud Reporting and Analytics- Utilize Data Cloud reports and dashboards
Topic 6: Data Cloud Setup and Administration- Manage Data Cloud permissions and security
- Configure Data Cloud settings
Topic 7: Data Ingestion and Modeling- Explain data modeling concepts including Data Model Objects (DMOs)
- Describe data streams and data sources

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Salesforce Data Cloud Accredited Professional Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
How does Identity Resolution select attributes for Unified Individuals when there is conflicting information in the Data Model?

Answer: C

Explanation:
Reconciliation rules are used to select attributes for Unified Individuals when there is conflicting information in the Data Model. Reconciliation rules can use different methods to prioritize values, such as source sequence, most recent value, or most frequent value. For example, if two profiles have different first names, reconciliation rules can determine which one to use for the unified profile based on the source sequence or the most recent value. References: Identity Resolution Reconciliation Rules


NEW QUESTION # 91
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?

Answer: B

Explanation:
A calculated insight is a multidimensional metric that is defined and calculated from data using SQL expressions. A calculated insight can include dimensions and measures. Dimensions are the fields that are used to group or filter the data, such as customer ID, product category, or region. Measures are the fields that are used to perform calculations or aggregations, such as revenue, quantity, or average order value. A calculated insight can be modified by editing the SQL expression or changing the data space. However, the consultant needs to be aware of the following limitations and considerations when modifying a calculated insight12:
Existing dimensions cannot be removed. If a dimension is removed from the SQL expression, the calculated insight will fail to run and display an error message. This is because the dimension is used to create the primary key for the calculated insight object, and removing it will cause a conflict with the existing data.
Therefore, the correct answer is B.
New dimensions can be added. If a dimension is added to the SQL expression, the calculated insight will run and create a new field for the dimension in the calculated insight object. However, the consultant should be careful not to add too many dimensions, as this can affect the performance and usability of the calculated insight.
Existing measures can be removed. If a measure is removed from the SQL expression, the calculated insight will run and delete the field for the measure from the calculated insight object. However, the consultant should be aware that removing a measure can affect the existing segments or activations that use the calculated insight.
New measures can be added. If a measure is added to the SQL expression, the calculated insight will run and create a new field for the measure in the calculated insight object. However, the consultant should be careful not to add too many measures, as this can affect the performance and usability of the calculated insight. References: Calculated Insights, Calculated Insights in a Data Space.


NEW QUESTION # 92
Cumulus Financial wants to create a segment of individuals based on transaction history data. This data has been mapped in the data model and is accessible via multiple container paths for segmentation.
What happens if the optimal container path for this use case is not selected?

Answer: A

Explanation:
In Salesforce Data Cloud, when segmenting individuals based on transaction history data, there may be multiple paths to the same data through different objects in the data model. If the wrong container path is selected:
The segment may pull in too many or too few individuals because different container paths may define relationships differently.
Some records may be unintentionally excluded or duplicated, affecting segmentation accuracy.
Identity resolution and relationships between objects might not behave as expected.
Why Not A? Data Cloud does not suggest alternate container paths automatically. The user must choose the correct path.
Why Not C? Data Cloud does not automatically select the optimal path; it relies on the user's selection.
Why Not D? The segment will still be generated but may have inaccurate results.
#Salesforce Data Cloud Reference:
Salesforce Help Documentation - Data Model and Segmentation Best Practices Trailhead Module: Segmentation in Data Cloud Salesforce Knowledge Base - Using Container Paths for Segmentation


NEW QUESTION # 93
Which feature can integrate in real time with Salesforce CRM?

Answer: B

Explanation:
Explanation
This feature can integrate in real time with Salesforce CRM. Data Actions allow you to perform actions on Salesforce CRM records based on Data Cloud data, such as creating, updating, or deleting records.
References:https://help.salesforce.com/s/articleView?id=sf.c360_a_data_actions.htm&type=5


NEW QUESTION # 94
An administrator is setting up a data stream with transactional dat
a. What field type should the administrator choose to ensure that leading zeros in the purchase order number are preserved?

Answer: A

Explanation:
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. Reference: [Field Types]


NEW QUESTION # 95
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