100% PASS EFFICIENT SALESFORCE - NEW DATA-CLOUD-CONSULTANT BRAINDUMPS EBOOK

100% Pass Efficient Salesforce - New Data-Cloud-Consultant Braindumps Ebook

100% Pass Efficient Salesforce - New Data-Cloud-Consultant Braindumps Ebook

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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

Salesforce Certified Data Cloud Consultant Sample Questions (Q116-Q121):

NEW QUESTION # 116
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud.
In what order should each process be run to ensure that freshly imported data is ready and available to use for any segment?

  • A. Refresh Data Stream > Calculated Insight > Identity Resolution
  • B. Identity Resolution > Refresh Data Stream > Calculated Insight
  • C. Calculated Insight > Refresh Data Stream > Identity Resolution
  • D. Refresh Data Stream > Identity Resolution > Calculated Insight

Answer: D

Explanation:
To ensure that freshly imported data from an Amazon S3 Bucket is ready and available to use for any segment, the following processes should be run in this order:
Refresh Data Stream: This process updates the data lake objects in Data Cloud with the latest data from the source system. It can be configured to run automatically or manually, depending on the data stream settings1. Refreshing the data stream ensures that Data Cloud has the most recent and accurate data from the Amazon S3 Bucket.
Identity Resolution: This process creates unified individual profiles by matching and consolidating source profiles from different data streams based on the identity resolution ruleset. It runs daily by default, but can be triggered manually as well2. Identity resolution ensures that Data Cloud has a single view of each customer across different data sources.
Calculated Insight: This process performs calculations on data lake objects or CRM data and returns a result as a new data object. It can be used to create metrics or measures for segmentation or analysis purposes3. Calculated insights ensure that Data Cloud has the derived data that can be used for personalization or activation.
Reference:
1: Configure Data Stream Refresh and Frequency - Salesforce
2: Identity Resolution Ruleset Processing Results - Salesforce
3: Calculated Insights - Salesforce


NEW QUESTION # 117
A customer creates a large segment of customers that placed orders in the last 30 days, and adds related attributes from the... to the activation. Upon checking the activation in Marketing Cloud, they notice It contains orders that are older than 30 days.
What should a consultant do to resolve this issue?

  • A. use data graphs that contain only 30 days of data.
  • B. Apply a data space fitter to exclude orders older than 30 days.
  • C. Use SQL in Marketing Cloud Engagement to remove orders older than 30 days.
  • D. Apply a filter to Purchase Order Date to exclude orders older than 30 days.

Answer: D

Explanation:
The issue arises because the activated segment in Marketing Cloud contains orders older than 30 days, despite the segment being defined to include only recent orders. The best solution is to apply a filter to the Purchase Order Date to exclude older orders. Here's why:
Understanding the Issue
The segment includes related attributes from the purchase order data.
Despite filtering for orders placed in the last 30 days, older orders are appearing in the activation.
Why Apply a Filter to Purchase Order Date?
Root Cause :
The related attributes (e.g., purchase order details) may not be filtered by the same criteria as the segment.
Without a specific filter on the Purchase Order Date , older orders may inadvertently be included.
Solution Approach :
Applying a filter directly to the Purchase Order Date ensures that only orders within the desired timeframe are included in the activation.
Other Options Are Less Suitable :
A . Use data graphs that contain only 30 days of data : Data graphs are not typically used to filter data for activations.
B . Apply a data space filter to exclude orders older than 30 days : Data space filters apply globally and may unintentionally affect other use cases.
D . Use SQL in Marketing Cloud Engagement to remove orders older than 30 days : This is a reactive approach and does not address the root cause in Data Cloud.
Steps to Resolve the Issue
Step 1: Review the Segment Definition
Confirm that the segment filters for orders placed in the last 30 days.
Step 2: Add a Filter to Purchase Order Date
Modify the activation configuration to include a filter on the Purchase Order Date , ensuring only orders within the last 30 days are included.
Step 3: Test the Activation
Publish the segment again and verify that the activation in Marketing Cloud contains only the desired orders.
Conclusion
By applying a filter to the Purchase Order Date , the consultant ensures that only orders placed in the last 30 days are included in the activation, resolving the issue effectively.


NEW QUESTION # 118
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?

  • A. Streaming data transform
  • B. Calculated insight
  • C. Batch bata transform
  • D. Streaming insight

Answer: D

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 # 119
A consultant notices that the unified individual profile is not storing the latest email address.
Which action should the consultant take to troubleshoot this issue?

  • A. Verify and update the email address in the source systems if needed.
  • B. Remove any old email addresses from Salesforce CRM.
  • C. Confirm that the reconciliation rules are correctly used.
  • D. Check if the mapping of DLO objects is correct to Contact Point Email.

Answer: C

Explanation:
Understanding Unified Individual Profile:
* The unified individual profile combines data from multiple sources to create a comprehensive view of each customer.


NEW QUESTION # 120
A global fashion retailer operates online sales platforms across AMFR, FMFA, and APAC. the data formats for customer, order, and product Information vary by region, and compliance regulations require data to remain unchanged in the original data sources They also require a unified view of customer profiles for real-time personalization and analytics.
Given these requirement, which transformation approach should the company implement to standardise and cleanse incoming data streams?

  • A. Implement streaming data transformations.
  • B. Transform data before ingesting into Data Cloud.
  • C. Implement batch data transformations.
  • D. Use Apex to transform and cleanse data.

Answer: A


NEW QUESTION # 121
......

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