DATA-CLOUD-CONSULTANT TEST QUESTIONS - NEW DATA-CLOUD-CONSULTANT EXAM LABS

Data-Cloud-Consultant Test Questions - New Data-Cloud-Consultant Exam Labs

Data-Cloud-Consultant Test Questions - New Data-Cloud-Consultant Exam Labs

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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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
  • 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.

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Salesforce Certified Data Cloud Consultant Sample Questions (Q44-Q49):

NEW QUESTION # 44
A customer has a calculated insight about lifetime value.
What does the consultant need to be aware of if the calculated insight.
needs to be modified?

  • A. Existing dimensions can be removed.
  • B. New measures can be added.
  • C. Existing measures can be removed.
  • D. New dimensions can be added.

Answer: A

Explanation:
A calculated insight is a multidimensional metric that is defined and calculated from data using SQL expressions. A calculated insight can include dimensions and measures. Dimensions are the fields that are used to group or filter the data, such as customer ID, product category, or region. Measures are the fields that are used to perform calculations or aggregations, such as revenue, quantity, or average order value. A calculated insight can be modified by editing the SQL expression or changing the data space. However, the consultant needs to be aware of the following limitations and considerations when modifying a calculated insight12:
Existing dimensions cannot be removed. If a dimension is removed from the SQL expression, the calculated insight will fail to run and display an error message. This is because the dimension is used to create the primary key for the calculated insight object, and removing it will cause a conflict with the existing data. Therefore, the correct answer is B.
New dimensions can be added. If a dimension is added to the SQL expression, the calculated insight will run and create a new field for the dimension in the calculated insight object. However, the consultant should be careful not to add too many dimensions, as this can affect the performance and usability of the calculated insight.
Existing measures can be removed. If a measure is removed from the SQL expression, the calculated insight will run and delete the field for the measure from the calculated insight object. However, the consultant should be aware that removing a measure can affect the existing segments or activations that use the calculated insight.
New measures can be added. If a measure is added to the SQL expression, the calculated insight will run and create a new field for the measure in the calculated insight object. However, the consultant should be careful not to add too many measures, as this can affect the performance and usability of the calculated insight. Reference: Calculated Insights, Calculated Insights in a Data Space.


NEW QUESTION # 45
What is the primary purpose of Data Cloud?

  • A. Integrating and unifying customer data
  • B. Analyzing marketing data results
  • C. Managing sales cycles and opportunities
  • D. Providing a golden record of a customer

Answer: A

Explanation:
* Primary Purpose of Data Cloud:
Salesforce Data Cloud's main function is to integrate and unify customer data from various sources, creating a single, comprehensive view of each customer.
Reference:
* Benefits of Data Integration and Unification:
Golden Record: Providing a unified, accurate view of the customer.
Enhanced Analysis: Enabling better insights and analytics through comprehensive data.
Improved Customer Engagement: Facilitating personalized and consistent customer experiences across channels.
* Steps for Data Integration:
Ingest data from multiple sources (CRM, marketing, service platforms).
Use data harmonization and reconciliation processes to unify data into a single profile.
* Practical Application:
Example: A retail company integrates customer data from online purchases, in-store transactions, and customer service interactions to create a unified customer profile.
This unified data enables personalized marketing campaigns and improved customer service.


NEW QUESTION # 46
A consultant needs to minimize the difference between a Data Cloud segment population and Marketing Cloud data extension count to determine the true size of segments for campaign planning.
What should the consultant recommend to filter the segments by to accomplish this?

  • A. Business units
  • B. Marketing Cloud Journeys
  • C. User preferences for marketing outreach
  • D. Geographical divisions

Answer: C

Explanation:
Segment Population vs. Data Extension Count: Minimizing the difference between Data Cloud segment populations and Marketing Cloud data extensions ensures accurate segment sizes for campaign planning.
Filtering by User Preferences: By filtering segments based on user preferences for marketing outreach, you ensure that only those contacts who have opted in or are eligible for marketing campaigns are included. This aligns the segment population in Data Cloud with the counts in Marketing Cloud.
Process:
* Define Preferences: Ensure that user preferences for marketing outreach are clearly defined and captured
* in the system.
* Filter Segments: Use these preferences to filter segments in Data Cloud, ensuring only the relevant contacts are included.
Benefits:
* Accuracy: Increases the accuracy of segment sizes by including only those who have opted in for marketing.
* Compliance: Helps in complying with regulatory requirements for marketing communications.
References:
* Salesforce Data Cloud Segmentation
* Marketing Cloud Data Extensions


NEW QUESTION # 47
A customer notices that their consolidation rate has recently increased. They contact the consultant to ask why.
What are two likely explanations for the increase?
Choose 2 answers

  • A. Identity resolution rules have been added to the ruleset to increase the number of matched
  • B. Identity resolution rules have been removed to reduce the number of matched profiles.
  • C. Duplicates have been removed from source system data streams.
  • D. New data sources have been added to Data Cloud that largely overlap with the existing profiles.

Answer: A,D

Explanation:
profiles.
Explanation:
The consolidation rate is a metric that measures the amount by which source profiles are combined to produce unified profiles in Data Cloud, calculated as 1 - (number of unified profiles / number of source profiles). A higher consolidation rate means that more source profiles are matched and merged into fewer unified profiles, while a lower consolidation rate means that fewer source profiles are matched and more unified profiles are created. There are two likely explanations for why the consolidation rate has recently increased for a customer:
New data sources have been added to Data Cloud that largely overlap with the existing profiles. This means that the new data sources contain many profiles that are similar or identical to the profiles from the existing data sources. For example, if a customer adds a new CRM system that has the same customer records as their old CRM system, the new data source will overlap with the existing one. When Data Cloud ingests the new data source, it will use the identity resolution ruleset to match and merge the overlapping profiles into unified profiles, resulting in a higher consolidation rate.
Identity resolution rules have been added to the ruleset to increase the number of matched profiles. This means that the customer has modified their identity resolution ruleset to include more match rules or more match criteria that can identify more profiles as belonging to the same individual. For example, if a customer adds a match rule that matches profiles based on email address and phone number, instead of just email address, the ruleset will be able to match more profiles that have the same email address and phone number, resulting in a higher consolidation rate.


NEW QUESTION # 48
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 use harmonized data to more accurately understand the customer and business impact
  • B. To eliminate the need for separate business intelligence and IT data management tools
  • C. To ingest and unify data from various sources to reconcile customer identity
  • D. To create and orchestrate cross-channel marketing messages

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 # 49
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