RELIABLE DATA-CLOUD-CONSULTANT EXAM QUESTIONS | DATA-CLOUD-CONSULTANT PASS RATE

Reliable Data-Cloud-Consultant Exam Questions | Data-Cloud-Consultant Pass Rate

Reliable Data-Cloud-Consultant Exam Questions | Data-Cloud-Consultant Pass Rate

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Tags: Reliable Data-Cloud-Consultant Exam Questions, Data-Cloud-Consultant Pass Rate, Accurate Data-Cloud-Consultant Answers, Test Data-Cloud-Consultant Topics Pdf, Valid Data-Cloud-Consultant Exam Format

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Data-Cloud-Consultant Pass Rate | Accurate Data-Cloud-Consultant Answers

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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 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 (Q28-Q33):

NEW QUESTION # 28
A Data Cloud Consultant Is in the process of setting up data streams for a new service-based data source.
When ingesting Case data, which field is recommended to be associated with the Event Time field?

  • A. Last Modified Date
  • B. Resolution Date
  • C. Creation Date
  • D. Escalation Date

Answer: A

Explanation:
The Event Time field is a special field type that captures the timestamp of an event in a data stream. It is used to track the chronological order of events and to enable time-based segmentation and activation. When ingesting Case data, the recommended field to be associated with the Event Time field is the Last Modified Date field. This field reflects the most recent update to the case and can be used to measure the case duration, resolution time, and customer satisfaction. The other fields, such as Resolution Date, Escalation Date, or Creation Date, are not as suitable for the Event Time field, as they may not capture the latest status of the case or may not be applicable for all cases. References: Data Stream Field Types, Salesforce Data Cloud Exam Questions


NEW QUESTION # 29
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plans to use Data Cloud and ingest the Contact object via the CRM Connector.
What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?

  • A. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object.
  • B. Ingest the Contact object and then create a calculated insight to normalize the phone numbers, and then map to the Contact Point Phone data map object.
  • C. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to the Contact Point Phone data map object from the Contact data stream.
  • D. Ingest the Contact object and create formula fields in the Contact data stream on the phone numbers, and then map to the Contact Point Phone data map object.

Answer: A

Explanation:
The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point. This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers.
The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streaming transforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the existing fields in the Contact object. Reference: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone


NEW QUESTION # 30
A user has built a segment in Data Cloud and is in the process of creating an activation. When selecting related attributes, they cannot find a specific set of attributes they know to be related to the individual.
Which statement explains why these attributes are not available?

  • A. The attributes are being used in another activation.
  • B. The segment is not segmenting on profile data.
  • C. Activations can only include 1-to-1 attributes.
  • D. The desired attributes reside on different related paths.

Answer: D

Explanation:
Explanation
The correct answer is C, the desired attributes reside on different related paths. When creating an activation in Data Cloud, you can select related attributes from data model objects that are linked to the segment entity.
However, not all related attributes are available for every activation. The availability of related attributes depends on the container path, which is the sequence of data model objects that connects the segment entity to the related entity. For example, if you segment on the Unified Individual entity, you can select related attributes from the Order Product entity, but only if the container path is Unified Individual > Order > Order Product. If the container path is Unified Individual > Order Line Item > Order Product, then the related attributes from Order Product are not available for activation. This is because Data Cloud only supports one-to-many relationships for related attributes, and Order Line Item is a many-to-many junction object between Order and Order Product. Therefore, you need to ensure that the desired attributes reside on the same related path as the segment entity, and that the path does not include any many-to-many junction objects. The other options are incorrect because they do not explain why the related attributes are not available. The segment entity can be any data model object, not just profile data. The attributes are not restricted by being used in another activation. Activations can include one-to-many attributes, not just one-to-one attributes. References:
* Related Attributes in Activation
* Considerations for Selecting Related Attributes
* Salesforce Launches: Data Cloud Consultant Certification
* Create a Segment in Data Cloud


NEW QUESTION # 31
Which configuration supports separate Amazon S3 buckets for data ingestion and activation?

  • A. Dedicated S3 data sources in Data Cloud setup
  • B. Separate user credentials for data stream and activation target
  • C. Dedicated S3 data sources in activation setup
  • D. Multiple S3 connectors in Data Cloud setup

Answer: A

Explanation:
Explanation
To support separate Amazon S3 buckets for data ingestion and activation, you need to configure dedicated S3 data sources in Data Cloud setup. Data sources are used to identify the origin and type of the data that you ingest into Data Cloud1. You can create different data sources for each S3 bucket that you want to use for ingestion or activation, and specify the bucket name, region, and access credentials2. This way, you can separate and organize your data by different criteria, such as brand, region, product, or business unit3. The other options are incorrect because they do not support separate S3 buckets for data ingestion and activation. Multiple S3 connectors are not a valid configuration in Data Cloud setup, as there is only one S3 connector available4. Dedicated S3 data sources in activation setup are not a valid configuration either, as activation setup does not require data sources, but activation targets5. Separate user credentials for data stream and activation target are not sufficient to support separate S3 buckets, as you also need to specify the bucket name and region for each data source2. References: Data Sources Overview, Amazon S3 Storage Connector, Data Spaces Overview, Data Streams Overview, Data Activation Overview


NEW QUESTION # 32
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?

  • A. Sales Order > Unified Individual
  • B. Unified Individual > Individual > Sales Order
  • C. Unified Individual > Unified Link Individual > Sales Order
  • D. Sales Order > Individual > Unified Individual

Answer: C

Explanation:
Explanation
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


NEW QUESTION # 33
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