Download Certified Data Cloud Consultant.Certified-Data-Cloud-Consultant.VCEplus.2024-11-14.84q.vcex

Vendor: Salesforce
Exam Code: Certified-Data-Cloud-Consultant
Exam Name: Certified Data Cloud Consultant
Date: Nov 14, 2024
File Size: 128 KB
Download Exam

How to open VCEX files?

Files with VCEX extension can be opened by ProfExam Simulator.

Download
ProfExam Simulator

Purchase
Coupon: EXAMFILESCOM

Discount: 20%

ProfExam Simulator ProfExam Simulator ProfExam Simulator ProfExam Simulator ProfExam Simulator

Demo Questions

Question 1
A consultant is helping a beauty company ingest its profile data into Data Cloud. The company's source data includes several fields, such as eye color, skin type, and hair color, that are not fields in the standard Individual data model object (DMO).
What should the consultant recommend to map this data to be used for both segmentation and identity resolution?
  1. Create a custom DMO from scratch that has all fields that are needed.
  2. Create a custom DMO with only the additional fields and map it to the standard Individual DMO.
  3. Create custom fields on the standard Individual DMO.
  4. Duplicate the standard Individual DMO and add the additional fields.
Correct answer: C
Explanation:
The best option to map the data to be used for both segmentation and identity resolution is to create custom fields on the standard Individual DMO.This way, the consultant can leverage the existing fields and functionality of the Individual DMO, such as identity resolution rulesets, calculated insights, and data actions, while adding the additional fields that are specific to the beauty company's data1. Creating a custom DMO from scratch or duplicating the standard Individual DMO would require more effort and maintenance, and might not be compatible with the existing features of Data Cloud. Creating a custom DMO with only the additional fields and mapping it to the standard Individual DMO would create unnecessary complexity and redundancy, and might not allow the use of the custom fields for identity resolution.1:Data Model Objects in Data Cloud  
The best option to map the data to be used for both segmentation and identity resolution is to create custom fields on the standard Individual DMO.This way, the consultant can leverage the existing fields and functionality of the Individual DMO, such as identity resolution rulesets, calculated insights, and data actions, while adding the additional fields that are specific to the beauty company's data1. Creating a custom DMO from scratch or duplicating the standard Individual DMO would require more effort and maintenance, and might not be compatible with the existing features of Data Cloud. Creating a custom DMO with only the additional fields and mapping it to the standard Individual DMO would create unnecessary complexity and redundancy, and might not allow the use of the custom fields for identity resolution.
1:Data Model Objects in Data Cloud
  
Question 2
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?
  1. Streaming data transform
  2. Streaming insight
  3. Calculated insight
  4. Batch bata transform
Correct answer: B
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.
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.
Question 3
A customer has multiple team members who create segment audiences that work in different time zones. One team member works at the home office in the Pacific time zone, that matches the org Time Zone setting. Another team member works remotely in the Eastern time zone.
Which user will see their home time zone in the segment and activation schedule areas?
  1. The team member in the Pacific time zone.
  2. The team member in the Eastern time zone.
  3. Neither team member; Data Cloud shows all schedules in GMT.
  4. Both team members; Data Cloud adjusts the segment and activation schedules to the time zone of the logged-in user
Correct answer: D
Explanation:
The correct answer is D, both team members; Data Cloud adjusts the segment and activation schedules to the time zone of the logged-in user. Data Cloud uses the time zone settings of the logged-in user to display the segment and activation schedules. This means that each user will see the schedules in their own home time zone, regardless of the org time zone setting or the location of other team members. This feature helps users to avoid confusion and errors when scheduling segments and activations across different time zones. The other options are incorrect because they do not reflect how Data Cloud handles time zones. The team member in the Pacific time zone will not see the same time zone as the org time zone setting, unless their personal time zone setting matches the org time zone setting. The team member in the Eastern time zone will not see the schedules in the org time zone setting, unless their personal time zone setting matches the org time zone setting. Data Cloud does not show all schedules in GMT, but rather in the user's local time zone.Data Cloud Time ZonesChange default time zones for Users and the organizationChange your time zone settings in Salesforce, Google & OutlookDateTime field and Time Zone Settings in Salesforce
The correct answer is D, both team members; Data Cloud adjusts the segment and activation schedules to the time zone of the logged-in user. Data Cloud uses the time zone settings of the logged-in user to display the segment and activation schedules. This means that each user will see the schedules in their own home time zone, regardless of the org time zone setting or the location of other team members. This feature helps users to avoid confusion and errors when scheduling segments and activations across different time zones. The other options are incorrect because they do not reflect how Data Cloud handles time zones. The team member in the Pacific time zone will not see the same time zone as the org time zone setting, unless their personal time zone setting matches the org time zone setting. The team member in the Eastern time zone will not see the schedules in the org time zone setting, unless their personal time zone setting matches the org time zone setting. Data Cloud does not show all schedules in GMT, but rather in the user's local time zone.
Data Cloud Time Zones
Change default time zones for Users and the organization
Change your time zone settings in Salesforce, Google & Outlook
DateTime field and Time Zone Settings in Salesforce
Question 4
How does Data Cloud ensure high availability and fault tolerance for customer data?
 
  1. By distributing data across multiple regions and data centers
  2. By using a data center with robust backups
  3. By Implementing automatic data recovery procedures
  4. By limiting data access to essential personnel
Correct answer: A
Explanation:
Ensuring High Availability and Fault Tolerance:High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.Data Distribution Across Multiple Regions and Data Centers:Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.Benefits of Regional Data Distribution:Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.Implementation in Salesforce Data Cloud:Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.
Ensuring High Availability and Fault Tolerance:
High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.
Data Distribution Across Multiple Regions and Data Centers:
Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.
If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.
Benefits of Regional Data Distribution:
Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.
Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.
Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.
Implementation in Salesforce Data Cloud:
Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.
This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.
Question 5
If a data source does not have a field that can be designated as a primary key, what should the consultant do?
  1. Use the default primary key recommended by Data Cloud.
  2. Create a composite key by combining two or more source fields through a formula field.
  3. Select a field as a primary key and then add a key qualifier.
  4. Remove duplicates from the data source and then select a primary key.
Correct answer: B
Explanation:
Understanding Primary Keys in Salesforce Data Cloud:A primary key is a unique identifier for records in a data source. It ensures that each record can be uniquely identified and accessed.Challenges with Missing Primary Keys:Some data sources may lack a natural primary key, making it difficult to uniquely identify records.Solution: Creating a Composite Key:Composite Key Definition: A composite key is created by combining two or more fields to generate a unique identifier.Formula Fields: Using a formula field, different fields can be concatenated to create a unique composite key.Example: If 'Email' and 'Phone Number' together uniquely identify a record, a formula field can concatenate these values to form a composite key.Steps to Create a Composite Key:Identify fields that, when combined, can uniquely identify each record.Create a formula field that concatenates these fields.Use this composite key as the primary key for the data source in Data Cloud.
Understanding Primary Keys in Salesforce Data Cloud:
A primary key is a unique identifier for records in a data source. It ensures that each record can be uniquely identified and accessed.
Challenges with Missing Primary Keys:
Some data sources may lack a natural primary key, making it difficult to uniquely identify records.
Solution: Creating a Composite Key:
Composite Key Definition: A composite key is created by combining two or more fields to generate a unique identifier.
Formula Fields: Using a formula field, different fields can be concatenated to create a unique composite key.
Example: If 'Email' and 'Phone Number' together uniquely identify a record, a formula field can concatenate these values to form a composite key.
Steps to Create a Composite Key:
Identify fields that, when combined, can uniquely identify each record.
Create a formula field that concatenates these fields.
Use this composite key as the primary key for the data source in Data Cloud.
Question 6
A Data Cloud customer wants to adjust their identity resolution rules to increase their accuracy of matches. Rather than matching on email address, they want to review a rule that joins their CRM Contacts with their Marketing Contacts, where both use the CRM ID as their primary key.
Which two steps should the consultant take to address this new use case?
Choose 2 answers
  1. Map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both.
  2. Map the primary key from the two systems to party identification, using CRM ID as the identification name for individuals coming from the CRM, and Marketing ID as the identification name for individuals coming fromthe marketing platform.
  3. Create a custom matching rule for an exact match on the Individual ID attribute.
  4. Create a matching rule based on party identification that matches on CRM ID as the party identification name.
Correct answer: AD
Explanation:
To address this new use case, the consultant should map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both, and create a matching rule based on party identification that matches on CRM ID as the party identification name. This way, the consultant can ensure that the CRM Contacts and Marketing Contacts are matched based on their CRM ID, which is a unique identifier for each individual. By using Party Identification, the consultant can also leverage the benefits of this attribute, such as being able to match across different entities and sources, and being able to handle multiple values for the same individual. The other options are incorrect because they either do not use the CRM ID as the primary key, or they do not use Party Identification as the attribute type.
To address this new use case, the consultant should map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both, and create a matching rule based on party identification that matches on CRM ID as the party identification name. This way, the consultant can ensure that the CRM Contacts and Marketing Contacts are matched based on their CRM ID, which is a unique identifier for each individual. By using Party Identification, the consultant can also leverage the benefits of this attribute, such as being able to match across different entities and sources, and being able to handle multiple values for the same individual. The other options are incorrect because they either do not use the CRM ID as the primary key, or they do not use Party Identification as the attribute type.
Question 7
Which consideration related to the way Data Cloud ingests CRM data is true?
  1. CRM data cannot be manually refreshed and must wait for the next scheduled synchronization,
  2. The CRM Connector's synchronization times can be customized to up to 15-minute intervals.
  3. Formula fields are refreshed at regular sync intervals and are updated at the next full refresh.
  4. The CRM Connector allows standard fields to stream into Data Cloud in real time.
Correct answer: D
Explanation:
The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time. This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization.This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.The other options are incorrect for the following reasons:A .CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.B .The CRM Connector's synchronization times can be customized to up to 60-minute intervals, not 15-minute intervals3. This option is false.C .Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered. This option is false.1:Connect and Ingest Data in Data Cloudarticle on Salesforce Help2:Data Sources in Data Cloudunit on Trailhead3:Data Cloud for Adminsmodule on Trailhead4: [Formula Fields in Data Cloud] unit on Trailhead5: [Data Streams in Data Cloud] unit on Trailhead
The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time. This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization.This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.
The other options are incorrect for the following reasons:
A .CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.
B .The CRM Connector's synchronization times can be customized to up to 60-minute intervals, not 15-minute intervals3. This option is false.
C .Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered. This option is false.
1:Connect and Ingest Data in Data Cloudarticle on Salesforce Help
2:Data Sources in Data Cloudunit on Trailhead
3:Data Cloud for Adminsmodule on Trailhead
4: [Formula Fields in Data Cloud] unit on Trailhead
5: [Data Streams in Data Cloud] unit on Trailhead
Question 8
What does the Source Sequence reconciliation rule do in identity resolution?
  1. Includes data from sources where the data is most frequently occurring
  2. Identifies which individual records should be merged into a unified profile by setting a priority for specific data sources
  3. Identifies which data sources should be used in the process of reconcillation by prioritizing the most recently updated data source
  4. Sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name 
Correct answer: D
Explanation:
: The Source Sequence reconciliation rule sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name. This rule allows you to define which data source should be used as the primary source of truth for each attribute, and which data sources should be used as fallbacks in case the primary source is missing or invalid. For example, you can set the Source Sequence rule to use data from Salesforce CRM as the first priority, data from Marketing Cloud as the second priority, and data from Google Analytics as the third priority for the first name attribute. This way, the unified profile will use the first name value from Salesforce CRM if it exists, otherwise it will use the value from Marketing Cloud, and so on. This rule helps you to ensure the accuracy and consistency of the unified profile attributes across different data sources.
: The Source Sequence reconciliation rule sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name. This rule allows you to define which data source should be used as the primary source of truth for each attribute, and which data sources should be used as fallbacks in case the primary source is missing or invalid. For example, you can set the Source Sequence rule to use data from Salesforce CRM as the first priority, data from Marketing Cloud as the second priority, and data from Google Analytics as the third priority for the first name attribute. This way, the unified profile will use the first name value from Salesforce CRM if it exists, otherwise it will use the value from Marketing Cloud, and so on. This rule helps you to ensure the accuracy and consistency of the unified profile attributes across different data sources.
Question 9
Which two dependencies prevent a data stream from being deleted?
Choose 2 answers
  1. The underlying data lake object is used in activation.
  2. The underlying data lake object is used in a data transform.
  3. The underlying data lake object is mapped to a data model object.
  4. The underlying data lake object is used in segmentation.
Correct answer: BC
Explanation:
To delete a data stream in Data Cloud, the underlying data lake object (DLO) must not have any dependencies or references to other objects or processes.The following two dependencies prevent a data stream from being deleted1:Data transform: This is a process that transforms the ingested data into a standardized format and structure for the data model. A data transform can use one or more DLOs as input or output.If a DLO is used in a data transform, it cannot be deleted until the data transform is removed or modified2.Data model object: This is an object that represents a type of entity or relationship in the data model. A data model object can be mapped to one or more DLOs to define its attributes and values.If a DLO is mapped to a data model object, it cannot be deleted until the mapping is removed or changed3.1:Delete a Data Streamarticle on Salesforce Help2: [Data Transforms in Data Cloud] unit on Trailhead3: [Data Model in Data Cloud] unit on Trailhead
To delete a data stream in Data Cloud, the underlying data lake object (DLO) must not have any dependencies or references to other objects or processes.The following two dependencies prevent a data stream from being deleted1:
Data transform: This is a process that transforms the ingested data into a standardized format and structure for the data model. A data transform can use one or more DLOs as input or output.If a DLO is used in a data transform, it cannot be deleted until the data transform is removed or modified2.
Data model object: This is an object that represents a type of entity or relationship in the data model. A data model object can be mapped to one or more DLOs to define its attributes and values.If a DLO is mapped to a data model object, it cannot be deleted until the mapping is removed or changed3.
1:Delete a Data Streamarticle on Salesforce Help
2: [Data Transforms in Data Cloud] unit on Trailhead
3: [Data Model in Data Cloud] unit on Trailhead
Question 10
What should a user do to pause a segment activation with the intent of using that segment again?
  1. Deactivate the segment.
  2. Delete the segment.
  3. Skip the activation.
  4. Stop the publish schedule.
Correct answer: A
Explanation:
The correct answer is A. Deactivate the segment. If a segment is no longer needed, it can be deactivated through Data Cloud and applies to all chosen targets.A deactivated segment no longer publishes, but it can be reactivated at any time1. This option allows the user to pause a segment activation with the intent of using that segment again.The other options are incorrect for the following reasons:B . Delete the segment.This option permanently removes the segment from Data Cloud and cannot be undone2. This option does not allow the user to use the segment again.C . Skip the activation.This option skips the current activation cycle for the segment, but does not affect the future activation cycles3. This option does not pause the segment activation indefinitely.D . Stop the publish schedule.This option stops the segment from publishing to the chosen targets, but does not deactivate the segment4. This option does not pause the segment activation completely.1:Deactivated Segmentarticle on Salesforce Help2:Delete a Segmentarticle on Salesforce Help 3:Skip an Activationarticle on Salesforce Help4:Stop a Publish Schedulearticle on Salesforce Help
The correct answer is A. Deactivate the segment. If a segment is no longer needed, it can be deactivated through Data Cloud and applies to all chosen targets.A deactivated segment no longer publishes, but it can be reactivated at any time1. This option allows the user to pause a segment activation with the intent of using that segment again.
The other options are incorrect for the following reasons:
B . Delete the segment.This option permanently removes the segment from Data Cloud and cannot be undone2. This option does not allow the user to use the segment again.
C . Skip the activation.This option skips the current activation cycle for the segment, but does not affect the future activation cycles3. This option does not pause the segment activation indefinitely.
D . Stop the publish schedule.This option stops the segment from publishing to the chosen targets, but does not deactivate the segment4. This option does not pause the segment activation completely.
1:Deactivated Segmentarticle on Salesforce Help
2:Delete a Segmentarticle on Salesforce Help 
3:Skip an Activationarticle on Salesforce Help
4:Stop a Publish Schedulearticle on Salesforce Help
CONNECT US
HOW TO OPEN VCE FILES

Use VCE Exam Simulator to open VCE files
Avanaset

HOW TO OPEN VCEX AND EXAM FILES

Use ProfExam Simulator to open VCEX and EXAM files
ProfExam Screen

ProfExam
ProfExam at a 20% markdown

You have the opportunity to purchase ProfExam at a 20% reduced price

Get Now!