Laden Sie die neuesten It-Pruefung Data-Cloud-Consultant PDF-Versionen von Prüfungsfragen kostenlos von Google Drive herunter: https://drive.google.com/open?id=1emGo7pNCKYtmkaqtqrV3_qirPPLLf0mX
Prüfungsfragen und Antworten zur Data-Cloud-Consultant Zertifizierung verändern sich immer wegen der Entwicklung der IT-Technik. Deshalb sind Dumps von It-Pruefung immer aktualisiert. Und wenn sie die Prüfungsunterlagen zur Salesforce Data-Cloud-Consultant Zertifizierung von It-Pruefung kaufen, bietet It-Pruefung Ihnen einjährigen kostlosen Aktualisierungsservice. Solange die exam Fragen aktualisiert sind, werden wir Ihnen die neuesten Data-Cloud-Consultant Prüfungsmaterialien senden. Damit können Sie jederzeit die neueste Version haben. It-Pruefung kann sowohl Ihnen helfen, die Prüfung zu bestehen, als auch die neuesten Kenntnisse zu beherrschen. Verpassen Sie bitte nicht preiswerte Unterlagen.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Act on Data / Activation | 18% | - Troubleshooting activation issues and dependencies - Data actions and integration with Salesforce products - Activation types and use cases |
| Topic 2: Identity Resolution and Harmonization | 14% | - Reconciliation and result analysis - Identity graphs and data unification - Matching rules (deterministic / probabilistic) |
| Topic 3: Segmentation and Insights | 18% | - Segment creation, configuration, and maintenance - AI-powered insights and analysis - Calculated vs streaming insights |
| Topic 4: Solution Positioning / Overview | 14% | - Data 360 / Data Cloud terminology and business value - Common use cases and data ethics principles - Role in AI and predictive capabilities |
| Topic 5: Setup and Administration | 13% | - Monitoring, reporting, and tools (Data Explorer, APIs) - Data streams, data spaces, and governance - Permissions, settings, and security configuration |
| Topic 6: Data Ingestion and Modeling | 20% | - Customer 360 data model and schema design - Data mapping, validation, and quality best practices - Ingestion methods, connectors, and transformation |
>> Data-Cloud-Consultant Online Praxisprüfung <<
Als der professionelle Lieferant der IT-Zertifizierungsunterlagen, bieten wir It-Pruefung immer die besten Unterlagen für Kandidaten und helfen vielen Leuten, die Salesforce Data-Cloud-Consultant Prüfung zu bestehen. Mit denen Salesforce Data-Cloud-Consultant Dumps von It-Pruefung können Sie mehr selbstbewusster werden. Bei guter Nutzung der Dumps können Sie in sehr kürzer Zeit, die Salesforce Data-Cloud-Consultant Prüfung zu bestehen. Finden Sie es unglaublich? Aber es ist wirklich. Wenn Sie diese Unterlagensfragen von It-Pruefung benutzen, können Sie das Wunder sehen.
16. Frage
A customer has a custom Customer Email c object related to the standard Contact object in Salesforce CRM.
This custom object
stores the email address a Contact that they want to use for activation.
To which data entity is mapped?
Antwort: D
Begründung:
The Contact Point_Email object is the data entity that represents an email address associated with an individual in Data Cloud. It is part of the Customer 360 Data Model, which is a standardized data model that defines common entities and relationships for customer data. The Contact Point_Email object can be mapped to any custom or standard object that stores email addresses in Salesforce CRM, such as the custom Customer Email__c object. The other options are not the correct data entities to map to because:
* A. The Contact object is the data entity that represents a person who is associated with an account that is
* a customer, partner, or competitor in Salesforce CRM. It is not the data entity that represents an email address in Data Cloud.
* C. The custom Customer Email__c object is not a data entity in Data Cloud, but a custom object in Salesforce CRM. It can be mapped to a data entity in Data Cloud, such as the Contact Point_Email object, but it is not a data entity itself.
* D. The Individual object is the data entity that represents a unique person in Data Cloud. It is the core entity for managing consent and privacy preferences, and it can be related to one or more contact points, such as email addresses, phone numbers, or social media handles. It is not the data entity that represents an email address in Data Cloud. References: Customer 360 Data Model: Individual and Contact Points - Salesforce, Contact Point_Email | Object Reference for the Salesforce Platform | Salesforce Developers,
[Contact | Object Reference for the Salesforce Platform | Salesforce Developers], [Individual | Object Reference for the Salesforce Platform | Salesforce Developers]
17. Frage
A user wants to be able to create a multi-dimensional metric to identify unified individual lifetime value (LTV).
Which sequence of data model object (DMO) joins is necessary within the calculated Insight to enable this calculation?
Antwort: A
Begründung:
To create a multi-dimensional metric to identify unified individual lifetime value (LTV), the sequence of data model object (DMO) joins that is necessary within the calculated Insight is Unified Individual > Unified Link Individual > Sales Order. This is because the Unified Individual DMO represents the unified profile of an individual or entity that is created by identity resolution1. The Unified Link Individual DMO represents the link between a unified individual and an individual from a source system2. The Sales Order DMO represents the sales order information from a source system3. By joining these three DMOs, you can calculate the LTV of a unified individual based on the sales order data from different source systems. The other options are incorrect because they do not join the correct DMOs to enable the LTV calculation. Option B is incorrect because the Individual DMO represents the source profile of an individual or entity from a source system, not the unified profile4. Option C is incorrect because the join order is reversed, and you need to start with the Unified Individual DMO to identify the unified profile. Option D is incorrect because it is missing the Unified Link Individual DMO, which is needed to link the unified profile with the source profile. References: Unified Individual Data Model Object, Unified Link Individual Data Model Object, Sales Order Data Model Object, Individual Data Model Object
18. Frage
What does the Ignore Empty Value option do in identity resolution?
Antwort: A
Begründung:
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
Data Cloud Identity Resolution Reconciliation Rule Input
Configure Identity Resolution Rulesets
Data and Identity in Data Cloud
19. Frage
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
Antwort: B,D
Begründung:
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.
Reference:
Learn How Data Cloud Works
About Salesforce Data Cloud
Discover Use Cases for the Platform
Understand Common Data Analysis Use Cases
20. Frage
Cumulus Financial uses Data Cloud to segment banking customers and activate them for direct mail via a Cloud File Storage activation. The company also wants to analyze individuals who have been in the segment within the last 2 years.
Which Data Cloud component allows for this?
Antwort: D
Begründung:
Data Cloud allows customers to analyze the segment membership history of individuals using the Segment Membership data model object. This object stores information about when an individual joined or left a segment, and can be used to create reports and dashboards to track segment performance over time. Cumulus Financial can use this object to filter individuals who have been in the segment within the last 2 years and compare them with other metrics.
The other options are not Data Cloud components that allow for this analysis. Segment exclusion is a feature that allows customers to remove individuals from a segment based on another segment. Nested segments are segments that are created from other segments using logical operators. Calculated insights are derived attributes that are created from existing data using formulas.
References:
* Segment Membership Data Model Object
* Data Cloud Reports and Dashboards
* Create a Segment in Data Cloud
21. Frage
......
It-Pruefung ist eine Website, die Fragenkataloge zur Data-Cloud-Consultant -Zertifizierungsprüfung bietet. Seine Erfolgsquote beträgt 100%. Das ist der Grund dafür, warum viele Kandiadaten It-Pruefung glauben. It-Pruefung kümmert sich immer um die Bedürfnisse der Kandidaten unf versuchen, ihre Bedürfnisse abzudecken. Mit It-Pruefung werden Sie sicher eine glänzende Zukunft haben.
Data-Cloud-Consultant Vorbereitung: https://www.it-pruefung.com/Data-Cloud-Consultant.html
P.S. Kostenlose und neue Data-Cloud-Consultant Prüfungsfragen sind auf Google Drive freigegeben von It-Pruefung verfügbar: https://drive.google.com/open?id=1emGo7pNCKYtmkaqtqrV3_qirPPLLf0mX