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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	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 3	Solution 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 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 (Q88-Q93):

NEW QUESTION # 88
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 ingest and unify data from various sources to reconcile customer identity
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 eliminate the need for separate business intelligence and IT data management tools

Answer: A,C

Explanation:
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.
References:
* Learn How Data Cloud Works
* About Salesforce Data Cloud
* Discover Use Cases for the Platform
* Understand Common Data Analysis Use Cases

NEW QUESTION # 89
Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use.
Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?

A. Package High Investment Balance Customers in a data kit.
B. Create new segments using nested segments.
C. Create a High Investment Balance calculated insight.
D. Create new segments by cloning High Investment Balance Customers.

Answer: B

Explanation:
Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:
* B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.
* C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.
* D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy. References: Create a Nested Segment - Salesforce, Save Time with Nested Segments (Generally Available) - Salesforce, Calculated Insights - Salesforce, Create and Publish a Data Kit Unit | Salesforce Trailhead, Create a Segment in Data Cloud - Salesforce

NEW QUESTION # 90
A consultant is discussing the benefits of Data Cloud with a customer that has multiple disjointed data sources.
Which two functional areas should the consultant highlight in relation to managing customer data?
Choose 2 answers

A. Data Marketplace
B. Data Harmonization
C. Master Data Management
D. Unified Profiles

Answer: B,D

Explanation:
Data Cloud is an open and extensible data platform that enables smarter, more efficient AI with secure access to first-party and industry data1. Two functional areas that the consultant should highlight in relation to managing customer data are:
* Data Harmonization: Data Cloud harmonizes data from multiple sources and formats into a common schema, enabling a single source of truth for customer data1. Data Cloud also applies data quality rules and transformations to ensure data accuracy and consistency.
* Unified Profiles: Data Cloud creates unified profiles of customers and prospects by linking data across different identifiers, such as email, phone, cookie, and device ID1. Unified profiles provide a holistic view of customer behavior, preferences, and interactions across channels and touchpoints. The other options are not correct because:
* Master Data Management: Master Data Management (MDM) is a process of creating and maintaining a single, consistent, and trusted source of master data, such as product, customer, supplier, or location data. Data Cloud does not provide MDM functionality, but it can integrate with MDM solutions to enrich customer data.
* Data Marketplace: Data Marketplace is a feature of Data Cloud that allows users to discover, access, and activate data from third-party providers, such as demographic, behavioral, and intent data. Data Marketplace is not a functional area related to managing customer data, but rather a source of external data that can enhance customer data. References:
* Salesforce Data Cloud
* [Data Harmonization for Data Cloud]
* [Unified Profiles for Data Cloud]
* [What is Master Data Management?]
* [Integrate Data Cloud with Master Data Management]
* [Data Marketplace for Data Cloud]

NEW QUESTION # 91
How does identity resolution select attributes for unified individuals when there Is conflicting information in the data model?

A. Creates additional rulesets
B. Leverages match rules
C. Leverages reconciliation rules
D. Creates additional contact points

Answer: C

Explanation:
Explanation
Identity resolution is the process of creating unified profiles of individuals by matching and merging data from different sources. When there is conflicting information in the data model, such as different names, addresses, or phone numbers for the same person, identity resolution leverages reconciliation rules to select the most accurate and complete attributes for the unified profile. Reconciliation rules are configurable rules that define how to resolve conflicts based on criteria such as recency, frequency, source priority, or completeness.
For example, a reconciliation rule can specify that the most recent name or the most frequent phone number should be selected for the unified profile. Reconciliation rules can be applied at the attribute level or the contact point level. References: Identity Resolution, Reconciliation Rules, Salesforce Data Cloud Exam Questions

NEW QUESTION # 92
Every day, Northern Trail Outfitters uploads a summary of the last 24 hours of store transactions to a new file in an Amazon S3 bucket, and files older than seven days are automatically deleted. Each file contains a timestamp in a standardized naming convention.
Which two options should a consultant configure when ingesting this data stream?
Choose 2 answers

A. Ensure the refresh mode is set to "Upsert".
B. Ensure the filename contains a wildcard to a accommodate the timestamp.
C. Ensure the refresh mode is set to "Full Refresh.''
D. Ensure that deletion of old files is enabled.

Answer: A,B

Explanation:
When ingesting data from an Amazon S3 bucket, the consultant should configure the following options:
* The refresh mode should be set to "Upsert", which means that new and updated records will be added or updated in Data Cloud, while existing records will be preserved. This ensures that the data is always up to date and consistent with the source.
* The filename should contain a wildcard to accommodate the timestamp, which means that the file name pattern should include a variable part that matches the timestamp format. For example, if the file name is store_transactions_2023-12-18.csv, the wildcard could be store_transactions_*.csv. This ensures that the ingestion process can identify and process the correct file every day.
The other options are not necessary or relevant for this scenario:
* Deletion of old files is a feature of the Amazon S3 bucket, not the Data Cloud ingestion process. Data Cloud does not delete any files from the source, nor does it require the source files to be deleted after ingestion.
* Full Refresh is a refresh mode that deletes all existing records in Data Cloud and replaces them with the records from the source file. This is not suitable for this scenario, as it would result in data loss and inconsistency, especially if the source file only contains the summary of the last 24 hours of
* transactions. References: Ingest Data from Amazon S3, Refresh Modes

NEW QUESTION # 93
......

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