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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

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.

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

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 4

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.

Salesforce Certified Data Cloud Consultant Sample Questions (Q28-Q33):

NEW QUESTION # 28
What does the Ignore Empty Value option do in identity resolution?

A. Ignores empty fields when running reconciliation rules
B. Ignores empty fields when running the standard match rules
C. Ignores empty fields when running any custom match rules
D. Ignores Individual object records with empty fields when running identity resolution rules

Answer: A

Explanation:
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
References:
* Data Cloud Identity Resolution Reconciliation Rule Input
* Configure Identity Resolution Rulesets
* Data and Identity in Data Cloud

NEW QUESTION # 29
A customer wants to create segments of users based on their Customer Lifetime Value.
However, the source data that will be brought into Data Cloud does not include that key performance indicator (KPI).
Which sequence of steps should the consultant follow to achieve this requirement?

A. Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation
B. Create Calculated Insight > Ingest Data > Map Data to Data Model> Use in Segmentation
C. Create Calculated Insight > Map Data to Data Model> Ingest Data > Use in Segmentation
D. Ingest Data > Create Calculated Insight > Map Data to Data Model > Use in Segmentation

Answer: A

Explanation:
Explanation
To create segments of users based on their Customer Lifetime Value (CLV), the sequence of steps that the consultant should follow is Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation. This is because the first step is to ingest the source data into Data Cloud using data streams1. The second step is to map the source data to the data model, which defines the structure and attributes of the data2. The third step is to create a calculated insight, which is a derived attribute that is computed based on the source or unified data3. In this case, the calculated insight would be the CLV, which can be calculated using a formula or a query based on the sales order data4. The fourth step is to use the calculated insight in segmentation, which is the process of creating groups of individuals or entities basedon their attributes and behaviors. By using the CLV calculated insight, the consultant can segment the users by their predicted revenue from the lifespan of their relationship with the brand. The other options are incorrect because they do not follow the correct sequence of steps to achieve the requirement. Option B is incorrect because it is not possible to create a calculated insight before ingesting and mapping the data, as the calculated insight depends on the data model objects3. Option C is incorrect because it is not possible to create a calculated insight before mapping the data, as the calculated insight depends on the data model objects3. Option D is incorrect because it is not recommended to create a calculated insight before mapping the data, as the calculated insight may not reflect the correct data model structure and attributes3. References: Data Streams Overview, Data Model Objects Overview, Calculated Insights Overview, Calculating Customer Lifetime Value (CLV) With Salesforce, [Segmentation Overview]

NEW QUESTION # 30
How does Data Cloud ensure data privacy and security?

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

Answer: D

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

NEW QUESTION # 31
A consultant needs to publish segment data to the Audience DMO that can be retrieved using the Query APIs.
When creating the activation target, which type of target should the consultant select?

A. External Activation Target
B. Marketing Cloud
C. Marketing Cloud Personalization
D. Data Cloud

Answer: A

Explanation:
Purpose of Activation Targets:
* Activation targets define where and how segment data is published for use in various applications and platforms.

NEW QUESTION # 32
Northern Trail Outfitters wants to implement Data Cloud and has several use cases in mind.
Which two use cases are considered a good fit for Data Cloud?
Choose 2 answers

A. To eliminate the need for separate business intelligence and IT data management tools
B. To create and orchestrate cross-channel marketing messages
C. To use harmonized data to more accurately understand the customer and business impact
D. To ingest and unify data from various sources to reconcile customer identity

Answer: C,D

Explanation:
Data Cloud is a data platform that can help customers connect, prepare, harmonize, unify, query, analyze, and act on their data across various Salesforce and external sources. Some of the use cases that are considered a good fit for Data Cloud are:
To ingest and unify data from various sources to reconcile customer identity. Data Cloud can help customers bring all their data, whether streaming or batch, into Salesforce and map it to a common data model. Data Cloud can also help customers resolve identities across different channels and sources and create unified profiles of their customers.
To use harmonized data to more accurately understand the customer and business impact. Data Cloud can help customers transform and cleanse their data before using it, and enrich it with calculated insights and related attributes. Data Cloud can also help customers create segments and audiences based on their data and activate them in any channel. Data Cloud can also help customers use AI to predict customer behavior and outcomes.
The other two options are not use cases that are considered a good fit for Data Cloud. Data Cloud does not provide features to create and orchestrate cross-channel marketing messages, as this is typically handled by other Salesforce solutions such as Marketing Cloud. Data Cloud also does not eliminate the need for separate business intelligence and IT data management tools, as it is designed to work with them and complement their capabilities.
Learn How Data Cloud Works
About Salesforce Data Cloud
Discover Use Cases for the Platform
Understand Common Data Analysis Use Cases

NEW QUESTION # 33
......

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