EXCELLENT SALESFORCE EXAMS TRAINING–100% PASS-RATE REVIEW DATA-CLOUD-CONSULTANT GUIDE

Excellent Salesforce Exams Training–100% Pass-Rate Review Data-Cloud-Consultant Guide

Excellent Salesforce Exams Training–100% Pass-Rate Review Data-Cloud-Consultant Guide

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The web-based Salesforce Data-Cloud-Consultant practice test software is designed explicitly for the Salesforce Certified Data Cloud Consultant exam. It is a well-known self-preparation tool that contains Data-Cloud-Consultant Exam Questions approved by Salesforce Certified Professionals. Our Salesforce Data-Cloud-Consultant exam questions are periodically updated and are similar to the real Salesforce Certified Data Cloud Consultant exam questions. The Salesforce Data-Cloud-Consultant Practice Test has a close resemblance with the actual Salesforce Data-Cloud-Consultant exam. Multiple This Salesforce certification exam needs to be finished in a certain time duration, therefore Salesforce Data-Cloud-Consultant practice test allows candidates to practice in the allocated time set according to their own needs.

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
  • 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 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
  • 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 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 (Q41-Q46):

NEW QUESTION # 41
A retailer wants to unify profiles using Loyalty ID which is different than the unique ID of their customers.
Which object should the consultant use in identity resolution to perform exact match rules on the Loyalty ID?

  • A. Party Identification object
  • B. Individual object
  • C. Loyalty Identification object
  • D. Contact Identification object

Answer: A

Explanation:
Explanation
The Party Identification object is the correct object to use in identity resolution to perform exact match rules on the Loyalty ID. The Party Identification object is a child object of the Individual object that stores different types of identifiers for an individual, such as email, phone, loyalty ID, social media handle, etc. Each identifier has a type, a value, and a source. The consultant can use the Party Identification object to create a match rule that compares the Loyalty ID type and value across different sources and links the corresponding individuals.
The other options are not correct objects to use in identity resolution to perform exact match rules on the Loyalty ID. The Loyalty Identification object does not exist in Data Cloud. The Individual object is the parent object that represents a unified profile of an individual, but it does not store the Loyalty ID directly. The Contact Identification objectis a child object of the Contact object that stores identifiers for a contact, such as email, phone, etc., but it does not store the Loyalty ID.
References:
* Data Modeling Requirements for Identity Resolution
* Identity Resolution in a Data Space
* Configure Identity Resolution Rulesets
* Map Required Objects
* Data and Identity in Data Cloud


NEW QUESTION # 42
A Data Cloud consultant is evaluating the initial phase of the Data Cloud lifecycle for a company.
Which action is essential to effectively begin the Data Cloud lifecycle?

  • A. Analyze and partition the data into data spaces.
  • B. Identify use cases and the required data sources and data quality.
  • C. Use calculated insights determine the benefits of Data Cloud for this company.
  • D. Migrate the existing data into the Customer 360 Data Model.

Answer: B

Explanation:
Data Cloud Lifecycle: The initial phase of the Salesforce Data Cloud lifecycle is critical for setting the foundation for successful data integration and utilization.
Identifying Use Cases:
* Importance: Defining clear use cases helps in understanding the business objectives and how Data Cloud can address them.
* Required Data Sources: Identifying the necessary data sources ensures that relevant data is ingested into Data Cloud.
* Data Quality: Assessing data quality is essential for accurate and reliable data analysis and insights.
Actions:
* Step 1: Engage with stakeholders to define specific use cases for Data Cloud.
* Step 2: Identify and catalog the required data sources for these use cases.
* Step 3: Evaluate the quality of data from these sources to ensure they meet the standards for effective data analysis.
References:
* Salesforce Data Cloud Implementation Guide
* Salesforce Data Cloud Lifecycle


NEW QUESTION # 43
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. Calculated Insight > Refresh Data Stream > Identity Resolution
  • B. Identity Resolution > Refresh Data Stream > Calculated Insight
  • C. Refresh Data Stream > Identity Resolution > Calculated Insight
  • D. Refresh Data Stream > Calculated Insight > Identity Resolution

Answer: C

Explanation:
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.
References:
* 1: Configure Data Stream Refresh and Frequency - Salesforce
* 2: Identity Resolution Ruleset Processing Results - Salesforce
* 3: Calculated Insights - Salesforce


NEW QUESTION # 44
What is the result of a segmentation criteria filtering on City | Is Equal To | 'San Jose'?

  • A. Cities only containing 'San Jose' or 'san jose'
  • B. Cities containing 'San Jose', 'San Jose', 'san jose', or 'san jose'
  • C. Cities only containing 'San Jose' or 'san jose'
  • D. Cities only containing 'San Jose' or 'San Jose'

Answer: C

Explanation:
The result of a segmentation criteria filtering on City | Is Equal To | 'San Jose' is cities only containing 'San Jose' or 'san jose'. This is because the segmentation criteria is case-sensitive and accent-sensitive, meaning that it will only match the exact value that is entered in the filter1. Therefore, cities containing 'San Jose', 'san jose', or 'San Jose' will not be included in the result, as they do not match the filter value exactly. To include cities with different variations of the name 'San Jose', you would need to use the OR operator and add multiple filter values, such as 'San Jose' OR 'San Jose' OR 'san jose' OR 'san jose'2. Reference: Segmentation Criteria, Segmentation Operators


NEW QUESTION # 45
Cumulus Financial wants to be able to track the daily transaction volume of each of its customers in real time and send out anotification as soon as it detects volume outside a customer's normal range.
What should a consultant do to accommodate this request?

  • A. Use a streaming insight paired with a data action
  • B. Use a calculated insight paired with a flow.
  • C. Use streaming data transform with a flow.
  • D. Use streaming data transform combined with a data action.

Answer: A

Explanation:
Explanation
A streaming insight is a type of insight that analyzes streaming data in real time and triggers actions based on predefined conditions. A data action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. By using a streaming insight paired with a data action, a consultant can accommodate Cumulus Financial's request to track the daily transaction volume of each customer and send out a notification when the volume is outside the normal range. A calculated insight is a type of insight that performs calculations on data in a data space and stores the results in a data extension. A streaming data transform is a type of data transform that applies transformations to streaming data in real time and stores the results in a data extension. A flow is a type of automation that executes a series of actions when triggered by an event, a schedule, or another flow. None of these options can achieve the same functionality as a streaming insight paired with a data action. References: Use Insights in Data Cloud Unit, Streaming Insights and Data Actions Use Cases, Streaming Insights and Data Actions Limits and Behaviors


NEW QUESTION # 46
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