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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.
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.
Topic 5	Act on Data: This topic defines activations and their basic use cases, using attributes and related attributes, identifying and analyzing timing dependencies affecting the Data Cloud lifecycle. Additionally it focuses on troubleshooting common problems with activations, and using data actions, including their requirements and intended use cases.

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Salesforce Certified Data 360 Consultant (Data-Con-101) Sample Questions (Q96-Q101):

NEW QUESTION # 96
Which two dependencies prevent a data stream from being deleted?
Choose 2 answers

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

Answer: A,C

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.
Reference:
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 # 97
A customer needs to integrate in real time with Salesforce CRM.
Which feature accomplishes this requirement?

A. Data actions and Lightning web components
B. Sales and Service bundle
C. Streaming transforms
D. Data model triggers

Answer: C

Explanation:
The correct answer is A. Streaming transforms. Streaming transforms are a feature of Data Cloud that allows real-time data integration with Salesforce CRM. Streaming transforms use the Data Cloud Streaming API to synchronize micro-batches of updates between the CRM data source and Data Cloud in near-real time1. Streaming transforms enable Data Cloud to have the most current and accurate CRM data for segmentation and activation2.
The other options are incorrect for the following reasons:
* B. Data model triggers. Data model triggers are a feature of Data Cloud that allows custom logic to be executed when data model objects are created, updated, or deleted3. Data model triggers do not integrate data with Salesforce CRM, but rather manipulate data within Data Cloud.
* C. Sales and Service bundle. Sales and Service bundle is a feature of Data Cloud that allows pre-built data streams, data model objects, segments, and activations for Sales Cloud and Service Cloud data
* sources4. Sales and Service bundle does not integrate data in real time with Salesforce CRM, but rather ingests data at scheduled intervals.
* D. Data actions and Lightning web components. Data actions and Lightning web components are features of Data Cloud that allow custom user interfaces and workflows to be built and embedded in Salesforce applications5. Data actions and Lightning web components do not integrate data with Salesforce CRM, but rather display and interact with data within Salesforce applications.
References:
* 1: Load Data into Data Cloud
* 2: [Data Streams in Data Cloud]
* 3: [Data Model Triggers in Data Cloud] unit on Trailhead
* 4: [Sales and Service Bundle in Data Cloud] unit on Trailhead
* 5: [Data Actions and Lightning Web Components in Data Cloud] unit on Trailhead
* : [Data Model in Data Cloud] unit on Trailhead
* : [Create a Data Model Object] article on Salesforce Help
* : [Data Sources in Data Cloud] unit on Trailhead
* : [Connect and Ingest Data in Data Cloud] article on Salesforce Help
* : [Data Spaces in Data Cloud] unit on Trailhead
* : [Create a Data Space] article on Salesforce Help
* : [Segments in Data Cloud] unit on Trailhead
* : [Create a Segment] article on Salesforce Help
* : [Activations in Data Cloud] unit on Trailhead
* : [Create an Activation] article on Salesforce Help

NEW QUESTION # 98
Northern Trail Outfitters (NTO) asks its Data Cloud consultant for a list of contacts who fit within a certain segment for a mailing campaign.
How should the consultant provide this list to NTO?

A. Create the segment and then click Download to obtain the segment membership details to provide to NTO.
B. Create the segment and then activate the segment to NTO's Salesforce CRM.
C. Create a new file storage activation target, create the segment, and then activate the segment to the new activation target.
D. Create the segment, select Email as the activation target, and activate the segment di nearly to NTO.

Answer: D

NEW QUESTION # 99
The leadership team at Cumulus Financial has determined that customers who deposited more than $250,000 in the last five years and are not using advisory services will be the central focus for all new campaigns in the next year.
Which features support this use case?

A. Calculated insight and segment
B. Streaming insight and data action
C. Calculated insight and data action
D. Streaming insight and segment

Answer: A

Explanation:
Understanding the Use Case:
* The leadership team wants to focus on customers who have deposited more than $250,000 in the last five years and are not using advisory services.

NEW QUESTION # 100
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 throw an error and store null values.
B. The target field will be able to hold both types of values.
C. The target field will only hold the time part and ignore the date part.
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 # 101
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