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Our Salesforce Exam Questions greatly help Salesforce Certified Data Cloud Consultant (Data-Con-101) exam candidates in their preparation. Our Salesforce Data-Con-101 practice questions are designed and verified by prominent and qualified Salesforce Certified Data Cloud Consultant (Data-Con-101) exam dumps preparation experts. The qualified Salesforce Certified Data Cloud Consultant (Data-Con-101) exam questions preparation experts strive hard and put all their expertise to ensure the top standard and relevancy of Data-Con-101 exam dumps topics.
NEW QUESTION # 94
The recruiting team at Cumulus Financial wants to identify which candidates have browsed the jobs page on its website at least twice within the last 24 hours. They want the information about these candidates to be available for segmentation in Data Cloud and the candidates added to their recruiting system.
Which feature should a consultant recommend to achieve this goal?
Answer: A
Explanation:
A streaming insight is a feature that allows users to create and monitor real-time metrics from streaming data sources, such as web and mobile events. A streaming insight can also trigger data actions, such as sending notifications, creating records, or updating fields, based on the metric values and conditions. Therefore, a streaming insight is the best feature to achieve the goal of identifying candidates who have browsed the jobs page on the website at least twice within the last 24 hours, and adding them to the recruiting system. The other options are incorrect because:
A streaming data transform is a feature that allows users to transform and enrich streaming data using SQL expressions, such as filtering, joining, aggregating, or calculating values. However, a streaming data transform does not provide the ability to monitor metrics or trigger data actions based on conditions.
A calculated insight is a feature that allows users to define and calculate multidimensional metrics from data using SQL expressions, such as LTV, CSAT, or average order value. However, a calculated insight is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions.
A batch data transform is a feature that allows users to create and schedule complex data transformations using a visual editor, such as joining, aggregating, filtering, or appending data. However, a batch data transform is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions. References: Streaming Insights, Create a Streaming Insight, Use Insights in Data Cloud, Learn About Data Cloud Insights, Data Cloud Insights Using SQL, Streaming Data Transforms, Get Started with Batch Data Transforms in Data Cloud, Transformations for Batch Data Transforms, Batch Data Transforms in Data Cloud: Quick Look, Salesforce Data Cloud: AI CDP.
NEW QUESTION # 95
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers
Answer: A,C
Explanation:
A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:
The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes.
The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.
Create a Calculated Insight, Use Insights in Data Cloud, Segmentation
NEW QUESTION # 96
A Data Cloud consultant tries to save a new 1-to-l relationship between the Account DMO and Contact Point Address DMO but gets an error.
What should the consultant do to fix this error?
Answer: B
NEW QUESTION # 97
A retail customer wants to bring customer data from different sources
and wants to take advantage of identity resolution so that it can be
used in segmentation.
On which entity should this be segmented for activation membership?
Answer: A
Explanation:
The correct answer is B, Unified Individual. A Unified Individual is a record that represents a customer across different data sources, created by applying identity resolution rulesets. Identity resolution rulesets are sets of match and reconciliation rules that define how to link and merge data from different sources based on common attributes. Data Cloud uses identity resolution rulesets to resolve data across multiple data sources and helps you create one record for each customer, regardless of where the data came from1. A retail customer who wants to bring customer data from different sources and use identity resolution for segmentation should segment on the Unified Individual entity, which contains the resolved and consolidated customer data. The other options are incorrect because they do not represent the resolved customer data across different sources. A Subscriber is a record that represents a customer who has opted in to receive marketing communications. A Unified Contact is a record that represents a customer who has a relationship with a specific business unit. An Individual is a record that represents a customer's profile data from a single data source. References:
Identity Resolution Ruleset Processing Results
Consider Data Implications for Segmentation
Prepare for your Salesforce Data Cloud Consultant Credential
AI-based Identity Resolution: Linking Diverse Customer Data
NEW QUESTION # 98
A bank collects customer data for its loan applicants and high net worth customers. A customer can be both a load applicant and a high net worth customer, resulting in duplicate data.
How should a consultant ingest and map this data in Data Cloud?
Answer: A
Explanation:
To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separate Data Lake Objects (DLOs) and map them to the Individual and Contact Point Email Data Model Objects (DMOs). Here's why and how this works:
Understanding the Problem :
Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.
To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.
Why Two DLOs?
By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).
Mapping both DLOs to the Individual and Contact Point Email DMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.
Steps to Implement This Solution :
Step 1: Create two DLOs-one for loan applicants and another for high net worth customers.
Step 2: Map both DLOs to the Individual DMO to consolidate customer profiles.
Step 3: Map the email fields from both DLOs to the Contact Point Email DMO to enable identity resolution based on email addresses.
Step 4: Configure identity resolution rules to merge duplicate records based on shared attributes like email.
Why Not Other Options?
A). Use a data transform to consolidate the data into one DLO: Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.
C). Ingest the data into two DLOs and then map to two custom DMOs: Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.
D). Ingest the data into one DLO and then map to one custom DMO: Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.
By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.
NEW QUESTION # 99
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