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Title: Valid Data-Con-101 Exam Syllabus£ü100% Pass£üReal Questions [Print This Page]
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Title: Valid Data-Con-101 Exam Syllabus£ü100% Pass£üReal Questions
This is a desktop-based Data-Con-101 practice exam software that doesn't require an internet connection except for license validation during purchase. The software provides Salesforce Certified Data Cloud Consultant (Data-Con-101) practice exams that are customizable, helping students prepare for the actual Data-Con-101 Exam. The team updates the Salesforce Data-Con-101 tests regularly and is available 24/7 to address any issues. Assessment records are saved for easy tracking. Windows computers support the desktop Salesforce Data-Con-101 practice exam software.
Salesforce Data-Con-101 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Act on Data: This domain focuses on leveraging Data Cloud data for downstream actions through activations and data actions. It covers working with attributes, managing timing dependencies, troubleshooting activation issues like errors and rejected counts, and understanding requirements for triggering automated processes.
Topic 2
  • Data Cloud Setup and Administration: This domain focuses on configuring and managing Data Cloud environments through permissions, data streams, data bundles, and data spaces. It also covers administrative tools and techniques for diagnosing and exploring data using reports, dashboards, flows, APIs, and explorer tools.
Topic 3
  • Data Ingestion and Modeling: This domain addresses bringing data into Data Cloud and structuring it properly through transformation, ingestion from various sources, and data mapping. It emphasizes best practices for modeling data to support identity resolution and validating ingested data using available tools.

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Salesforce Certified Data Cloud Consultant Sample Questions (Q90-Q95):NEW QUESTION # 90
A customer wants to create segments of users based on their Customer Lifetime Value.
However, the source data that will be brought into Data Cloud does not include that key performance indicator (KPI).
Which sequence of steps should the consultant follow to achieve this requirement?
Answer: B
Explanation:
To create segments of users based on their Customer Lifetime Value (CLV), the sequence of steps that the consultant should follow is Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation. This is because the first step is to ingest the source data into Data Cloud using data streams1. The second step is to map the source data to the data model, which defines the structure and attributes of the data2. The third step is to create a calculated insight, which is a derived attribute that is computed based on the source or unified data3. In this case, the calculated insight would be the CLV, which can be calculated using a formula or a query based on the sales order data4. The fourth step is to use the calculated insight in segmentation, which is the process of creating groups of individuals or entities based on their attributes and behaviors. By using the CLV calculated insight, the consultant can segment the users by their predicted revenue from the lifespan of their relationship with the brand. The other options are incorrect because they do not follow the correct sequence of steps to achieve the requirement. Option B is incorrect because it is not possible to create a calculated insight before ingesting and mapping the data, as the calculated insight depends on the data model objects3. Option C is incorrect because it is not possible to create a calculated insight before mapping the data, as the calculated insight depends on the data model objects3. Option D is incorrect because it is not recommended to create a calculated insight before mapping the data, as the calculated insight may not reflect the correct data model structure and attributes3. References: Data Streams Overview, Data Model Objects Overview, Calculated Insights Overview, Calculating Customer Lifetime Value (CLV) With Salesforce, [Segmentation Overview]

NEW QUESTION # 91
Northern Trail Outfitters is using the Marketing Cloud Starter Data Bundles to bring Marketing Cloud data into Data Cloud.
What are two of the available datasets in Marketing Cloud Starter Data Bundles?
Choose 2 answers
Answer: A,B
Explanation:
The Marketing Cloud Starter Data Bundles are predefined data bundles that allow you to easily ingest data from Marketing Cloud into Data Cloud1. The available datasets in Marketing Cloud Starter Data Bundles are Email, MobileConnect, and MobilePush2. These datasets contain engagement events and metrics from different Marketing Cloud channels, such as email, SMS, and push notifications2. By using these datasets, you can enrich your Data Cloud data model with Marketing Cloud data and create segments and activations based on your marketing campaigns and journeys1. The other options are incorrect because they are not available datasets in Marketing Cloud Starter Data Bundles. Option A is incorrect because Personalization is not a dataset, but a feature of Marketing Cloud that allows you to tailor your content and messages to your audience3. Option C is incorrect because Loyalty Management is not a dataset, but a product of Marketing Cloud that allows you to create and manage loyalty programs for your customers4. References: Marketing Cloud Starter Data Bundles in Data Cloud, Connect Your Data Sources, Personalization in Marketing Cloud, Loyalty Management in Marketing Cloud

NEW QUESTION # 92
A company is seeking advice from a consultant on how to address the challenge of having multiple leads and contacts in Salesforce that share the same email address. The consultant wants to provide a detailed and comprehensive explanation on how Data Cloud can be leveraged to effectively solve this issue.
What should the consultant highlight to address this company's business challenge?
Answer: B
Explanation:
Issue Overview: When multiple leads and contacts share the same email address in Salesforce, it can lead to data duplication, inaccurate customer views, and inefficient marketing and sales efforts.
Data Cloud Identity Resolution: Salesforce Data Cloud offers Identity Resolution as a powerful tool to address this issue. It helps in merging and unifying data from multiple sources to create a single, comprehensive customer profile.
Process:
Data Ingestion: Import lead and contact data into Salesforce Data Cloud.
Identity Resolution Rules: Configure Identity Resolution rules to match and merge records based on key identifiers like email addresses.
Unification: The tool consolidates records that share the same email address, eliminating duplicates and ensuring a single view of each customer.
Continuous Updates: As new data comes in, Identity Resolution continuously updates and maintains the unified profiles.
Benefits:
Accurate Customer View: Reduces duplicate records and provides a complete view of each customer's interactions and history.
Improved Efficiency: Streamlines marketing and sales efforts by targeting a unified customer profile.
References:
Salesforce Data Cloud Identity Resolution
Salesforce Help: Identity Resolution Overview

NEW QUESTION # 93
The Salesforce CRM Connector is configured and the Case object data stream is set up. Subsequently, a new custom field named Business Priority is created on the Case object in Salesforce CRM. However, the new field is not available when trying to add it to the data stream.
Which statement addresses the cause of this issue?
Answer: D
Explanation:
The Salesforce CRM Connector uses the Salesforce Integration User to access the data from the Salesforce CRM org. The Integration User must have the Read permission on the fields that are included in the data stream. If the Integration User does not have the Read permission on the newly created field, the field will not be available for selection in the data stream configuration. To resolve this issue, the administrator should assign the Read permission on the new field to the Integration User profile or permission set. References: Create a Salesforce CRM Data Stream, Edit a Data Stream, Salesforce Data Cloud Full Refresh for CRM, SFMC, or Ingestion API Data Streams

NEW QUESTION # 94
A user is not seeing suggested values from newly-modeled data when building a segment.
What is causing this issue?
Answer: A
Explanation:
Value suggestion is a feature that allows users to see suggested values for data model object (DMO) fields when creating segment filters. However, this feature can take up to 24 hours to process and display the values for newly-modeled data. Therefore, if a user is not seeing suggested values from newly-modeled data, it is likely that the value suggestion is still processing and will be available soon. The other options are incorrect because value suggestion does not require any specific permissions, can work on both direct and related attributes, and can return more than 50 values for a specific attribute, depending on the data type and frequency of the values. References: Use Value Suggestions in Segmentation, Data Cloud Limits and Guidelines

NEW QUESTION # 95
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