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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

Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.

Topic 3

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 4

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.

Salesforce Certified Data Cloud Consultant Sample Questions (Q91-Q96):

NEW QUESTION # 91
A client wants to bring in loyalty data from a custom object in Salesforce CRM that contains a point balance for accrued hotel points and airline points within the same record. The client wants to split these point systems into two separate records for better tracking and processing.
What should a consultant recommend in this scenario?

A. Use batch transforms to create a second data lake object.
B. Clone the data source object.
C. Create a junction object in Salesforce CRM and modify the ingestion strategy.
D. Create a data kit from the data lake object and deploy it to the same Data Cloud org.

Answer: A

Explanation:
Batch transforms are a feature that allows creating new data lake objects based on existing data lake objects and applying transformations on them. This can be useful for splitting, merging, or reshaping data to fit the data model or business requirements. In this case, the consultant can use batch transforms to create a second data lake object that contains only the airline points from the original loyalty data object. The original object can be modified to contain only the hotel points. This way, the client can have two separate records for each point system and track and process them accordingly. References: Batch Transforms, Create a Batch Transform

NEW QUESTION # 92
How does Data Cloud ensure high availability and fault tolerance for customer data?

A. By distributing data across multiple regions and data centers
B. By Implementing automatic data recovery procedures
C. By using a data center with robust backups
D. By limiting data access to essential personnel

Answer: A

Explanation:
Ensuring High Availability and Fault Tolerance:
* High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.

NEW QUESTION # 93
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. Calculated Insight > Refresh Data Stream > Identity Resolution
B. Identity Resolution > Refresh Data Stream > Calculated Insight
C. Refresh Data Stream > Calculated Insight > Identity Resolution
D. Refresh Data Stream > Identity Resolution > Calculated Insight

Answer: D

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 # 94
What are the two minimum requirements needed when using the Visual Insights Builder to create a calculated insight?
Choose 2 answers

A. At least two objects to Join
B. At least one dimension
C. A WHERE clause
D. At least one measure

Answer: B,D

Explanation:
* Introduction to Visual Insights Builder:
The Visual Insights Builder in Salesforce Data Cloud is a tool used to create calculated insights, which are custom metrics derived from the existing data.
Reference:
* Requirements for Creating Calculated Insights:
Measure: A measure is a quantitative value that you want to analyze, such as revenue, number of purchases, or total time spent on a platform.
Dimension: A dimension is a qualitative attribute that you use to categorize or filter the measures, such as date, region, or customer segment.
* Steps to Create a Calculated Insight:
Navigate to the Visual Insights Builder within Salesforce Data Cloud.
Select "Create New Insight" and choose the dataset.
Add at least one measure: This could be any metric you want to analyze, such as "Total Sales." Add at least one dimension: This helps to break down the measure, such as "Sales by Region."
* Practical Application:
Example: To create an insight on "Average Purchase Value by Region," you would need:
A measure: Total Purchase Value.
A dimension: Customer Region.
This allows for actionable insights, such as identifying high-performing regions.

NEW QUESTION # 95
Which information is provided in a .csv file when activating to Amazon S3?

A. The metadata regarding the segment definition
B. The activated data payload
C. The manifest of origin sources within Data Cloud
D. An audit log showing the user who activated the segment and when it was activated

Answer: B

Explanation:
When activating to Amazon S3, the information that is provided in a .csv file is the activated data payload. The activated data payload is the data that is sent from Data Cloud to the activation target, which in this case is an Amazon S3 bucket1. The activated data payload contains the attributes and values of the individuals or entities that are included in the segment that is being activated2. The activated data payload can be used for various purposes, such as marketing, sales, service, or analytics3. The other options are incorrect because they are not provided in a .csv file when activating to Amazon S3. Option A is incorrect because an audit log is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Activation History tab4. Option C is incorrect because the metadata regarding the segment definition is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Segmentation tab5. Option D is incorrect because the manifest of origin sources within Data Cloud is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Data Sources tab. References: Data Activation Overview, Create and Activate Segments in Data Cloud, Data Activation Use Cases, View Activation History, Segmentation Overview, [Data Sources Overview]

NEW QUESTION # 96
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