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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.

Topic 4

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.

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Salesforce Certified Data Cloud Consultant Sample Questions (Q148-Q153):

NEW QUESTION # 148
How does Data Cloud ensure data privacy and security?

A. BY limiting data access to authorized admins
B. By encrypting data at rest and in transit
C. By enforcing and controlling consent references
D. By securely storing data in an offsite server

Answer: B

Explanation:
Data Privacy and Security in Data Cloud:
* Ensuring data privacy and security is paramount in Salesforce Data Cloud.

NEW QUESTION # 149
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud. Based on this, a calculated insight is created that shows the total spend per customer in the last 30 days.
In which sequence should each process be run to ensure that freshly imported data is ready and available to use for any segment?

A. Identity Resolution > Refresh Data Stream > Calculated Insight
B. Refresh Data Stream > Calculated Insight > Identity Resolution
C. Refresh Data Stream > Identity Resolution > Calculated Insight
D. Calculated Insight > Refresh Data Stream > Identity Resolution

Answer: C

Explanation:
To ensure that freshly imported data is ready and available for use in any segment, the processes should be run in the following sequence: Refresh Data Stream > Identity Resolution > Calculated Insight . Here's why:
Understanding the Requirement
Northern Trail Outfitters uploads new customer data daily to an Amazon S3 bucket, which is ingested into Data Cloud.
A calculated insight is created to show the total spend per customer in the last 30 days.
The goal is to ensure that the data is properly refreshed, resolved, and processed before being used in segments.
Why This Sequence?
Step 1: Refresh Data Stream
Before any processing can occur, the data stream must be refreshed to ingest the latest data from the Amazon S3 bucket.
This ensures that the most up-to-date customer data is available in Data Cloud.
Step 2: Identity Resolution
After refreshing the data stream, identity resolution must be performed to merge related records into unified profiles.
This step ensures that customer data is consolidated and ready for analysis.
Step 3: Calculated Insight
Once identity resolution is complete, the calculated insight can be generated to calculate the total spend per customer in the last 30 days.
This ensures that the insight is based on the latest and most accurate data.
Other Options Are Incorrect :
B . Refresh Data Stream > Calculated Insight > Identity Resolution : Calculated insights cannot be generated before identity resolution because they rely on unified profiles.
C . Calculated Insight > Refresh Data Stream > Identity Resolution : Calculated insights require both fresh data and resolved identities, so this sequence is invalid.
D . Identity Resolution > Refresh Data Stream > Calculated Insight : Identity resolution cannot occur without first refreshing the data stream to bring in the latest data.
Conclusion
The correct sequence is Refresh Data Stream > Identity Resolution > Calculated Insight , ensuring that the data is properly refreshed, resolved, and processed before being used in segments.

NEW QUESTION # 150
Northern Trail Outfitters (NTO) owns and operates six unique brands, each with their own set of customers, transactions, and loyalty information. The marketing director wants to ensure that segments and activations from the NTO Outlet brand do not reference customers or transactions from the other brands.
What is the most efficient approach to handle this requirement?

A. Separate the Outlet brand into a data space.
B. Use Business Unit Aware activation.
C. Separate the brands into six different data spaces.
D. Create a batch data transform to generate a DLO for the Outlet brand.

Answer: A

Explanation:
To ensure segments and activations for the NTO Outlet brand do not reference data from other brands, the most efficient approach is to isolate the Outlet brand's data using Data Spaces. Here's the analysis:
Data Spaces (Option B):
Definition: Data Spaces in Salesforce Data Cloud partition data into isolated environments, ensuring that segments, activations, and analytics only reference data within the same space.
Why It Works: By creating a dedicated Data Space for the Outlet brand, all customer, transaction, and loyalty data for Outlet will be siloed. Segments and activations built in this space cannot access data from other brands, even if they exist in the same Data Cloud instance.
Efficiency: This avoids complex filtering logic or manual data management. It aligns with Salesforce's best practice of using Data Spaces for multi-brand or multi-entity organizations (Source: Salesforce Data Cloud Implementation Guide, "Data Partitioning with Data Spaces").
Why Other Options Are Incorrect:
Business Unit Aware Activation (A):
Business Unit (BU) settings in Salesforce CRM control record visibility but are not natively tied to Data Cloud segmentation.
BU-aware activation ensures activations respect sharing rules but does not prevent segments from referencing data across BUs in Data Cloud.
Six Different Data Spaces (C):
While creating a Data Space for each brand (6 total) would technically isolate all data, the requirement specifically focuses on the Outlet brand. Creating six spaces is unnecessary overhead and not the "most efficient" solution.
Batch Data Transform to Generate DLO (D):
Creating a Data Lake Object (DLO) via batch transforms would require ongoing manual effort to filter Outlet-specific data and does not inherently prevent cross-brand references in segments.
Steps to Implement:
Step 1: Navigate to Data Cloud Setup > Data Spaces and create a new Data Space for the Outlet brand.
Step 2: Ingest Outlet-specific data (customers, transactions, loyalty) into this Data Space.
Step 3: Build segments and activations within the Outlet Data Space. The system will automatically restrict access to other brands' data.
Conclusion: Separating the Outlet brand into its own Data Space (Option B) is the most efficient way to enforce data isolation and meet the requirement. This approach leverages native Data Cloud functionality without overcomplicating the setup.

NEW QUESTION # 151
Which two dependencies prevent a data stream from being deleted?
Choose 2 answers

A. The underlying data lake object is used in segmentation.
B. The underlying data lake object is used in activation.
C. The underlying data lake object is used in a data transform.
D. The underlying data lake object is mapped to a data model object.

Answer: C,D

Explanation:
Explanation
To delete a data stream in Data Cloud, the underlying data lake object (DLO) must not have any dependencies or references to other objects or processes. The following two dependencies prevent a data stream from being deleted1:
* Data transform: This is a process that transforms the ingested data into a standardized format and structure for the data model. A data transform can use one or more DLOs as input or output. If a DLO is used in a data transform, it cannot be deleted until the data transform is removed or modified2.
* Data model object: This is an object that represents a type of entity or relationship in the data model. A data model object can be mapped to one or more DLOs to define its attributes and values. If a DLO is mapped to a data model object, it cannot be deleted until the mapping is removed or changed3.
References:
* 1: Delete a Data Stream article on Salesforce Help
* 2: [Data Transforms in Data Cloud] unit on Trailhead
* 3: [Data Model in Data Cloud] unit on Trailhead

NEW QUESTION # 152
A consultant at Northern Trail Outfitters is attempting to ingest a field from the Contact object in Salesforce CRM that contains both yyyy-mm-dd and yyyy-mm-dd hh:mm:ss values. The target field is set to Date datatype.
Which statement is true in this situation?

A. The target field will only hold the time part and ignore the date part.
B. The target field will be able to hold both types of values.
C. The target field will throw an error and store null values.
D. The target field will only hold the date part and ignore the time part.

Answer: D

Explanation:
Field Data Types: Salesforce CRM's Contact object fields can store data in various formats. When ingesting data into Salesforce Data Cloud, the target field's data type determines how the data is processed and stored.
Date Data Type: If the target field in Data Cloud is set to Date data type, it is designed to store date values without time information.
Mixed Format Values: When ingesting a field containing both date (yyyy-mm-dd) and datetime (yyyy-mm-dd hh:mm:ss) values into a Date data type field:
* The Date field will extract and store only the date part (yyyy-mm-dd), ignoring the time part (hh:mm:ss).
Result:
* Date Values: yyyy-mm-dd values are stored as-is.
* Datetime Values: yyyy-mm-dd hh:mm:ss values are truncated to yyyy-mm-dd, and the time component is ignored.
References:
* Salesforce Data Cloud Field Mapping
* Salesforce Data Types

NEW QUESTION # 153
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