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Detailed Data-Cloud-Consultant Study Dumps & Data-Cloud-Consultant Test Cram Review

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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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

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 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.

Salesforce Certified Data Cloud Consultant Sample Questions (Q79-Q84):

NEW QUESTION # 79
A Data Cloud consultant recently discovered that their identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual.
What should the consultant do to address this issue?

A. Modify the existing ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
B. Modify the existing ruleset with stricter matching criteria, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.
C. Create and run a new ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
D. Create and run a new rules fewer matching rules, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.

Answer: C

Explanation:
Explanation
Identity resolution is the process of linking source profiles from different data sources into unified individual profiles based on match and reconciliation rules. If the identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual, it means that the match rules are too loose and need to be refined. The best way to address this issue is to create and run a new ruleset with stricter matching criteria, such as adding more attributes or increasing the match score threshold. Then, the consultant can compare the two rulesets to review and verify the results, and see if the new ruleset reduces the false positives and improves the accuracy of the identity resolution. Once the new ruleset is approved, the consultant can migrate to the new ruleset and delete the old one. The other options are incorrect because modifying the existing ruleset can affect the existing unified profiles and cause data loss or inconsistency.
Creating and running a new ruleset with fewer matching rules can increase the false negatives and reduce the coverage of the identity resolution. References: Create Unified Individual Profiles, AI-based Identity Resolution: Linking Diverse Customer Data, Data Cloud Identiy Resolution.

NEW QUESTION # 80
Which data model subject area defines the revenue or quantity for an opportunity by product family?

A. Product
B. Engagement
C. Sales Order
D. Party

Answer: C

Explanation:
The Sales Order subject area defines the details of an order placed by a customer for one or more products or services. It includes information such as the order date, status, amount, quantity, currency, payment method, and delivery method. The Sales Order subject area also allows you to track the revenue or quantity for an opportunity by product family, which is a grouping of products that share common characteristics or features. For example, you can use the Sales Order Line Item DMO to associate each product in an order with its product family, and then use the Sales Order Revenue DMO to calculate the total revenue or quantity for each product family in an opportunity. Reference: Sales Order Subject Area, Sales Order Revenue DMO Reference

NEW QUESTION # 81
How does Data Cloud ensure high availability and fault tolerance for customer data?

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

Answer: C

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.
Reference:
* Data Distribution Across Multiple Regions and Data Centers:
Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.
If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.
* Benefits of Regional Data Distribution:
Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.
Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.
Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.
* Implementation in Salesforce Data Cloud:
Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.
This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.

NEW QUESTION # 82
When trying to disconnect a data source an error will be generated if it has which two dependencies associated with it?
Choose 2 answers

A. Segment
B. Data stream
C. Activation target
D. Activation

Answer: A,B

Explanation:
When disconnecting a data source in Salesforce Data Cloud, the system checks for active dependencies that rely on the data source. Based on Salesforce's official documentation (Disconnect a Data Source), the error occurs if the data source has data streams or segments associated with it. Here's the breakdown:
Key Dependencies That Block Disconnection
Data Stream (Option B):
Why It Matters:
A data stream is the pipeline that ingests data from the source into Data Cloud. If an active data stream is connected to the data source, disconnecting the source will fail because the stream depends on it for ongoing data ingestion.
Resolution:
Delete or pause the data stream first.
Documentation Reference:
"Before disconnecting a data source, delete all data streams that are associated with it." (Salesforce Help Article) Segment (Option C):
Why It Matters:
Segments built using data from the source will reference that data source. Disconnecting the source would orphan these segments, so the system blocks the action.
Resolution:
Delete or modify segments that depend on the data source.
Documentation Reference:
"If there are segments that use data from the data source, you must delete those segments before disconnecting the data source." (Salesforce Help Article) Why Other Options Are Incorrect Activation (A):
Activations send segments to external systems (e.g., Marketing Cloud) but do not directly depend on the data source itself. The dependency chain is Segment → Activation, not Data Source → Activation.
Activation Target (D):
Activation targets (e.g., Marketing Cloud) are destinations and do not tie directly to the data source.
Steps to Disconnect a Data Source
Delete Dependent Segments:
Navigate to Data Cloud > Segments and remove any segments built using the data source.
Delete or Pause Data Streams:
Go to Data Cloud > Data Streams and delete streams linked to the data source.
Disconnect the Data Source:
Once dependencies are resolved, disconnect the source via Data Cloud > Data Sources.

NEW QUESTION # 83
Cumulus Financial needs to create a composite key on an incoming data source that combines the fields Customer Region and Customer Identifier.
Which formula function should a consultant use to create a composite key when a primary key is not available in a data stream?

A. CONCAT
B. COMBIN
C. CAST
D. COALE

Answer: A

Explanation:
Composite Keys in Data Streams: When working with data streams in Salesforce Data Cloud, there may be situations where a primary key is not available. In such cases, creating a composite key from multiple fields ensures unique identification of records.
Formula Functions: Salesforce provides several formula functions to manipulate and combine data fields.
Among them, the CONCAT function is used to combine multiple strings into one.
Creating Composite Keys: To create a composite key using CONCAT, a consultant can combine the values of Customer Region and Customer Identifier into a single unique identifier.
* Example Formula: CONCAT(Customer_Region, Customer_Identifier)
References:
* Salesforce Documentation: Formula Functions
* Salesforce Data Cloud Guide

NEW QUESTION # 84
......

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