Data-Cloud-Consultant試験問題集合格できるには更新された2025年10月テスト問題集
Data-Cloud-Consultantテスト問題練習は2025年最新のに更新された170問あります
Salesforce Data-Cloud-Consultant 認定試験の出題範囲：
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質問 # 86
What does the Ignore Empty Value option do in identity resolution?
- A. Ignores empty fields when running reconciliation rules
- B. Ignores Individual object records with empty fields when running identity resolution rules
- C. Ignores empty fields when running any custom match rules
- D. Ignores empty fields when running the standard match rules
正解：A
解説：
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
質問 # 87
A customer needs to integrate in real time with Salesforce CRM.
Which feature accomplishes this requirement?
- A. Data model triggers
- B. Streaming transforms
- C. Data actions and Lightning web components
- D. Sales and Service bundle
正解：B
解説：
The correct answer is A. Streaming transforms. Streaming transforms are a feature of Data Cloud that allows real-time data integration with Salesforce CRM. Streaming transforms use the Data Cloud Streaming API to synchronize micro-batches of updates between the CRM data source and Data Cloud in near-real time1. Streaming transforms enable Data Cloud to have the most current and accurate CRM data for segmentation and activation2.
The other options are incorrect for the following reasons:
B: Data model triggers. Data model triggers are a feature of Data Cloud that allows custom logic to be executed when data model objects are created, updated, or deleted3. Data model triggers do not integrate data with Salesforce CRM, but rather manipulate data within Data Cloud.
C: Sales and Service bundle. Sales and Service bundle is a feature of Data Cloud that allows pre-built data streams, data model objects, segments, and activations for Sales Cloud and Service Cloud data sources4. Sales and Service bundle does not integrate data in real time with Salesforce CRM, but rather ingests data at scheduled intervals.
D: Data actions and Lightning web components. Data actions and Lightning web components are features of Data Cloud that allow custom user interfaces and workflows to be built and embedded in Salesforce applications5. Data actions and Lightning web components do not integrate data with Salesforce CRM, but rather display and interact with data within Salesforce applications.
References:
1: Load Data into Data Cloud
2: [Data Streams in Data Cloud]
3: [Data Model Triggers in Data Cloud] unit on Trailhead
4: [Sales and Service Bundle in Data Cloud] unit on Trailhead
5: [Data Actions and Lightning Web Components in Data Cloud] unit on Trailhead
6: [Data Model in Data Cloud] unit on Trailhead
7: [Create a Data Model Object] article on Salesforce Help
8: [Data Sources in Data Cloud] unit on Trailhead
9: [Connect and Ingest Data in Data Cloud] article on Salesforce Help
10: [Data Spaces in Data Cloud] unit on Trailhead
11: [Create a Data Space] article on Salesforce Help
12: [Segments in Data Cloud] unit on Trailhead
13: [Create a Segment] article on Salesforce Help
14: [Activations in Data Cloud] unit on Trailhead
15: [Create an Activation] article on Salesforce Help
質問 # 88
The marketing manager at Cloud Kicks plans to bring in corporate phone numbers for its accounts into Data Cloud. They plan to use a custom field with data set to Phone to store these phone numbers.
Which statement is true when ingesting phone numbers?
- A. The phone number field car only accept 10-digit values.
- B. Data Cloud validates the format of the phone number at the time of Ingestion.
- C. The phone number field should be used as a primary key.
- D. Text value can be accepted for ingestion into = phone data type field.
正解：D
解説：
When ingesting phone numbers into a custom field with the Phone data type in Salesforce Data Cloud, the correct statement is that text values can be accepted for ingestion into a phone data type field . Here's why:
Understanding the Requirement
The marketing manager at Cloud Kicks plans to ingest corporate phone numbers into Data Cloud using a custom field with the Phone data type.
It is important to understand how phone numbers are validated and stored during ingestion.
Why Text Values Can Be Accepted?
Phone Data Type Behavior :
The Phone data type in Salesforce accepts text values, as phone numbers are typically stored as strings (e.g.,
"+1-800-555-1234").
While the field is designed for phone numbers, it does not enforce strict formatting rules during ingestion.
Validation During Ingestion :
Salesforce does not validate the format of phone numbers at the time of ingestion.
Validation occurs only when the data is used in downstream systems or applications that enforce formatting rules.
Other Options Are Incorrect :
B). Data Cloud validates the format of the phone number at the time of ingestion : This is incorrect because Data Cloud does not validate phone number formats during ingestion.
C). The phone number field can only accept 10-digit values : This is incorrect because the Phone data type supports various formats, including international numbers.
D). The phone number field should be used as a primary key : This is incorrect because phone numbers are not unique identifiers and should not be used as primary keys.
Steps to Ingest Phone Numbers
Step 1: Create a Custom Field
Navigate to Object Manager > Account > Fields & Relationships and create a custom field with the Phone data type.
Step 2: Configure Data Ingestion
Ensure the source data includes phone numbers as text values.
Map the phone number field from the source to the custom field in Data Cloud.
Step 3: Validate Data Usage
Test the ingested data to ensure it meets downstream requirements (e.g., formatting for dialing).
Conclusion
Text values can be accepted for ingestion into a Phone data type field, as phone numbers are stored as strings and formatting validation occurs later in the process.
質問 # 89
If a data source does not have a field that can be designated as a primary key, what should the consultant do?
- A. Use the default primary key recommended by Data Cloud.
- B. Select a field as a primary key and then add a key qualifier.
- C. Create a composite key by combining two or more source fields through a formula field.
- D. Remove duplicates from the data source and then select a primary key.
正解：C
解説：
* 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.
Reference:
* 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.
質問 # 90
Cumulus Financial wants to segregate Salesforce CRM Account data based on Country for its Data Cloud users.
What should the consultant do to accomplish this?
- A. Use Salesforce sharing rules on the Account object to filter and segregate records based on Country.
- B. Use formula fields based on the account Country field to filter incoming records.
- C. Use the data spaces feature and applying filtering on the Account data lake object based on Country.
- D. Use streaming transforms to filter out Account data based on Country and map to separate data model objects accordingly.
正解：C
解説：
Data spaces are a feature that allows Data Cloud users to create subsets of data based on filters and permissions. Data spaces can be used to segregate data based on different criteria, such as geography, business unit, or product line. In this case, the consultant can use the data spaces feature and apply filtering on the Account data lake object based on Country. This way, the Data Cloud users can access only the Account data that belongs to their respective countries. Reference: Data Spaces, Create a Data Space
質問 # 91
A global fashion retailer operates online sales platforms across AMFR, FMFA, and APAC. the data formats for customer, order, and product Information vary by region, and compliance regulations require data to remain unchanged in the original data sources They also require a unified view of customer profiles for real-time personalization and analytics.
Given these requirement, which transformation approach should the company implement to standardise and cleanse incoming data streams?
- A. Implement streaming data transformations.
- B. Implement batch data transformations.
- C. Transform data before ingesting into Data Cloud.
- D. Use Apex to transform and cleanse data.
正解：A
質問 # 92
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. Escalation Date
- B. Creation Date
- C. Resolution Date
- D. Last Modified Date
正解：D
解説：
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
質問 # 93
When creating a segment on an individual, what is the result of using two separate containers linked by an AND as shown below?
GoodsProduct | Count | At Least | 1
Color | Is Equal To | red
AND
GoodsProduct | Count | At Least | 1
PrimaryProductCategory | Is Equal To | shoes
- A. Individuals who purchased at least one of any 'red' product or purchased at least one pair of
'shoes' - B. Individuals who purchased at least one of any red' product and also purchased at least one pair of 'shoes'
- C. Individuals who made a purchase of at least one 'red shoes' and nothing else
- D. Individuals who purchased at least one 'red shoes' as a single line item in a purchase
正解：B
解説：
Explanation
When creating a segment on an individual, using two separate containers linked by an AND means that the individual must satisfy both the conditions in the containers. In this case, the individual must have purchased at least one product with the color attribute equal to 'red' and at least one product with the primary product category attribute equal to 'shoes'. The products do not have to be the same or purchased in the same transaction. Therefore, the correct answer is A.
The other options are incorrect because they imply different logical operators or conditions. Option B implies that the individual must have purchased a single product that has both the color attribute equal to 'red' and the primary product category attribute equal to 'shoes'. Option C implies that the individual must have purchased only one product that has both the color attribute equal to 'red' and the primary product category attribute equal to 'shoes' and no other products. Option D implies that the individual must have purchased either one product with the color attribute equal to 'red' or one product with the primary product category attribute equal to 'shoes' or both, which is equivalent to using an OR operator instead of an AND operator.
References:
* Create a Container for Segmentation
* Create a Segment in Data Cloud
* Navigate Data Cloud Segmentation
質問 # 94
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?
- A. Batch bata transform
- B. Calculated insight
- C. Streaming data transform
- D. Streaming insight
正解：D
解説：
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. References: Streaming Insights, Create a Streaming Insight, Use Insights in Data Cloud, Learn About Data Cloud Insights, Data Cloud Insights Using SQL, Streaming Data Transforms, Get Started with Batch Data Transforms in Data Cloud, Transformations for Batch Data Transforms, Batch Data Transforms in Data Cloud: Quick Look, Salesforce Data Cloud: AI CDP.
質問 # 95
A consultant is reviewing a recent activation using engagement-based related attributes but is not seeing any related attributes in their payload for the majority of their segment members.
Which two areas should the consultant review to help troubleshoot this issue?
Choose 2 answers
- A. The activated profiles have a Unified Contact Point.
- B. The activations are referencing segments that segment on profile data rather than engagement data.
- C. The correct path is selected for the related attributes.
- D. The related engagement events occurred within the last 90 days.
正解：C、D
解説：
Explanation
Engagement-based related attributes are attributes that describe the interactions of a person with an email message, such as opens, clicks, unsubscribes, etc. These attributes are stored in the Engagement data model object (DMO) and can be added to an activation to send more personalized communications. However, there are some considerations and limitations when using engagement-based related attributes, such as:
* For engagement data, activation supports a 90-day lookback window. This means that only the attributes from the engagement events that occurred within the last 90 days are considered for activation. Any records outside of this window are not included in the activation payload. Therefore, the consultant should review the event time of the related engagement events and make sure they are within the lookback window.
* The correct path to the related attributes must be selected for the activation. A path is a sequence of DMOs that are connected by relationships in the data model. For example, the path from Individual to Engagement is Individual -> Email -> Engagement. The path determines which related attributes are available for activation and how they are filtered. Therefore, the consultant should review the path selection and make sure it matches the desired related attributes and filters.
The other two options are not relevant for this issue. The activations can reference segments that segment on profile data rather than engagement data, as long as the activation target supports related attributes. The activated profiles do not need to have a Unified Contact Point, which is a unique identifier for a person across different data sources, to activate engagement-based related attributes. References: Add Related Attributes to an Activation, Related Attributes in Data Cloud activation have no values, Explore the Engagement Data Model Object
質問 # 96
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 segment is not segmenting on profile data.
- B. Activations can only include 1-to-1 attributes.
- C. The attributes are being used in another activation.
- D. The desired attributes reside on different related paths.
正解：D
解説：
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
質問 # 97
A consultant is planning the ingestion of a data stream that has profile information including a mobile phone number.
To ensure that the phone number can be used for future SMS campaigns, they need to confirm the phone number field is in the proper E164 Phone Number format. However, the phone numbers in the file appear to be in varying formats.
What is the most efficient way to guarantee that the various phone number formats are standardized?
- A. Create a calculated insight after ingestion.
- B. Assign the PhoneNumber field type when creating the data stream.
- C. Create a formula field to standardize the format.
- D. Edit and update the data in the source system prior to sending to Data Cloud.
正解：B
解説：
The most efficient way to guarantee that the various phone number formats are standardized is to assign the PhoneNumber field type when creating the data stream. The PhoneNumber field type is a special field type that automatically converts phone numbers into the E164 format, which is the international standard for phone numbers. The E164 format consists of a plus sign (+), the country code, and the national number. For example,
+1-202-555-1234 is the E164 format for a US phone number. By using the PhoneNumber field type, the consultant can ensure that the phone numbers are consistent and can be used for future SMS campaigns. The other options are either more time-consuming, require manual intervention, or do not address the formatting issue. References: Data Stream Field Types, E164 Phone Number Format, Salesforce Data Cloud Exam Questions
質問 # 98
A consultant wants to build a new audience in Data Cloud.
Which three criteria can the consultant include when building a segment?
Choose 3 answers
- A. Direct attributes
- B. Related attributes
- C. Data stream attributes
- D. Streaming insights
- E. Calculated Insights
正解：A、B、E
解説：
A segment is a subset of individuals who meet certain criteria based on their attributes and behaviors. A consultant can use different types of criteria when building a segment in Data Cloud, such as:
Direct attributes: These are attributes that describe the characteristics of an individual, such as name, email, gender, age, etc. These attributes are stored in the Profile data model object (DMO) and can be used to filter individuals based on their profile data.
Calculated Insights: These are insights that perform calculations on data in a data space and store the results in a data extension. These insights can be used to segment individuals based on metrics or scores derived from their data, such as customer lifetime value, churn risk, loyalty tier, etc.
Related attributes: These are attributes that describe the relationships of an individual with other DMOs, such as Email, Engagement, Order, Product, etc. These attributes can be used to segment individuals based on their interactions or transactions with different entities, such as email opens, clicks, purchases, etc.
The other two options are not valid criteria for building a segment in Data Cloud. Data stream attributes are attributes that describe the streaming data that is ingested into Data Cloud from various sources, such as Marketing Cloud, Commerce Cloud, Service Cloud, etc. These attributes are not directly available for segmentation, but they can be transformed and stored in data extensions using streaming data transforms.
Streaming insights are insights that analyze streaming data in real time and trigger actions based on predefined conditions. These insights are not used for segmentation, but for activation and personalization. References: Create a Segment in Data Cloud, Use Insights in Data Cloud, Data Cloud Data Model
質問 # 99
A healthcare client wants to make use of identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII).
Which matching rule criteria should a consultant recommend for the most accurate matching results?
- A. Party Identification on Patient ID
- B. Fuzzy First Name, Exact Last Name, and Email
- C. Email Address and Phone
- D. Exact Last Name and Emil
正解：A
解説：
Explanation
Identity resolution is the process of linking data from different sources into a unified profile of a customer or an individual. Identity resolution uses matching rules to compare the attributes of different records and determine if they belong to the same person. Matching rules can be based on exact or fuzzy matching of various attributes, such as name, email, phone, address, or custom identifiers. A healthcare client who wants to use identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII), such as name or email, should use a matching rule criteria that is based on a unique and reliable identifier that is specific to the healthcare domain. One such identifier is the patient ID, which is a unique number assigned to each patient by a healthcare provider or system. By using the party identification on patient ID as a matching rule criteria, the healthcare client can ensure that only records that have the same patient ID are matched and unified, and avoid false positives or false negatives that may occur due to common or similar names or emails. The party identification on patient ID is also a secure and compliant way of handling sensitive healthcare data, as it does not expose or share any PII that may be subject to data protection regulations or standards. References: Configure Identity Resolution Rulesets, A framework of identity resolution: evaluating identity attributes and methods
質問 # 100
A consultant wants to make sure address details from customer orders are selected as best to save to the unified profile.
What should the consultant do to achieve this?
- A. Select the address details on the Contact Point Address. Change the reconciliation rules for the specific address attributes to Source Priority and move the Individual DMO to the bottom.
- B. Change the default reconciliation rules for Individual to Source Priority.
- C. Select the address details on the Contact Point Address. Change the reconciliation rules for the specific address attributes to Source Priority and move the Oder DMO to the top.
- D. Use the default reconciliation rules for Contact Point Address.
正解：C
解説：
Unified Profile: Creating a unified customer profile in Salesforce Data Cloud involves consolidating data from various sources.
Reconciliation Rules: These rules determine which data source is considered the "best" when conflicting data is encountered. Changing reconciliation rules allows prioritizing specific sources.
Source Priority: Setting source priority involves defining which data source should be preferred over others for specific attributes.
Process:
Step 1: Access the Data Cloud settings for reconciliation rules.
Step 2: Select the Contact Point Address details.
Step 3: Change the reconciliation rules for address attributes to "Source Priority." Step 4: Move the Order DMO to the top of the priority list. This ensures that address details from customer orders are prioritized and selected as the best data to save to the unified profile.
Benefits:
Accuracy: Ensures the most accurate and reliable address data is used in the unified profile.
Relevance: Gives priority to the most relevant and frequently updated source (customer orders).
References:
Salesforce Data Cloud Reconciliation Rules
Salesforce Unified Customer Profile
質問 # 101
A consultant is reviewing a recent activation using engagement-based related attributes but is not seeing any related attributes in their payload for the majority of their segment members.
Which two areas should the consultant review to help troubleshoot this issue?
Choose 2 answers
- A. The activated profiles have a Unified Contact Point.
- B. The activations are referencing segments that segment on profile data rather than engagement data.
- C. The correct path is selected for the related attributes.
- D. The related engagement events occurred within the last 90 days.
正解：C、D
解説：
Engagement-based related attributes are attributes that describe the interactions of a person with an email message, such as opens, clicks, unsubscribes, etc. These attributes are stored in the Engagement data model object (DMO) and can be added to an activation to send more personalized communications. However, there are some considerations and limitations when using engagement-based related attributes, such as:
For engagement data, activation supports a 90-day lookback window. This means that only the attributes from the engagement events that occurred within the last 90 days are considered for activation. Any records outside of this window are not included in the activation payload. Therefore, the consultant should review the event time of the related engagement events and make sure they are within the lookback window.
The correct path to the related attributes must be selected for the activation. A path is a sequence of DMOs that are connected by relationships in the data model. For example, the path from Individual to Engagement is Individual -> Email -> Engagement. The path determines which related attributes are available for activation and how they are filtered. Therefore, the consultant should review the path selection and make sure it matches the desired related attributes and filters.
The other two options are not relevant for this issue. The activations can reference segments that segment on profile data rather than engagement data, as long as the activation target supports related attributes. The activated profiles do not need to have a Unified Contact Point, which is a unique identifier for a person across different data sources, to activate engagement-based related attributes. References: Add Related Attributes to an Activation, Related Attributes in Data Cloud activation have no values, Explore the Engagement Data Model Object
質問 # 102
A marketing manager at Northern Trail Outfitters wants to Improve marketing return on investment (ROI) by tapping into Insights from Data Cloud Segment Intelligence.
Which permission set does a user need to set this up?
- A. Data Cloud Data Aware Specialist
- B. Cloud Marketing Manager
- C. Data Cloud User
- D. Data Cloud Admin
正解：D
質問 # 103
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 segment is not segmenting on profile data.
- B. Activations can only include 1-to-1 attributes.
- C. The attributes are being used in another activation.
- D. The desired attributes reside on different related paths.
正解：D
解説：
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
質問 # 104
A user needs permissions to access Data Cloud to create, manage, and activate segments, However, the user should not be allowed to created reports or manage data sources.
Which permission set should the consultant assign?
- A. Data Cloud Data Aware Specialist
- B. Data Cloud Marketing Specialist
- C. Data Cloud Marketing Manager
- D. Data Cloud user
正解：B
質問 # 105
Cumulus Financial segregates its sales CRM data based on Region for its Data Cloud users. Multiple data spaces are configured: a default space and two additional spaces tailored for EMEA and APAC regions.
EME A sales reps who need temporary access to visualize data for both regions say that they cannot visualize APAC dat a. APAC sales reps can visualize the corresponding segmented data.
Which statement describes the cause of this issue?
- A. The APAC data space is not associated with any permission set.
- B. The EMEA sales reps have not been assigned to the profile associated with the APAC data space.
- C. The APAC data space Is not associated with any profile.
- D. The EMEA sales reps have not been assigned to the permission set associated with the APAC data space.
正解：D
質問 # 106
During an implementation project, a consultant completed ingestion of all data streams for their customer.
Prior to segmenting and acting on that data, which additional configuration is required?
- A. Calculated Insights
- B. Data Activation
- C. Identity Resolution
- D. Data Mapping
正解：C
解説：
Explanation
After ingesting data from different sources into Data Cloud, the additional configuration that is required before segmenting and acting on that data is Identity Resolution. Identity Resolution is the process of matching and reconciling source profiles from different data sources and creating unified profiles that represent a single individual or entity1. Identity Resolution enables you to create a 360-degree view of your customers and prospects, and to segment and activate them based on their attributes and behaviors2. To configure Identity Resolution, you need to create and deploy a ruleset that defines the match rules and reconciliation rules for your data3. The other options are incorrect because they are not required before segmenting and acting on the data. Data Activation is the process of sending data from Data Cloud to other Salesforce clouds or external destinations for marketing, sales, or service purposes4. Calculated Insights are derived attributes that are computed based on the source or unified data, such as lifetime value, churn risk, or product affinity5. Data Mapping is the process of mapping source attributes to unified attributes in the data model. These configurations can be done after segmenting and acting on the data, or in parallel with Identity Resolution, but they are not prerequisites for it. References: Identity Resolution Overview, Segment and Activate Data in Data Cloud, Configure Identity Resolution Rulesets, Data Activation Overview, Calculated Insights Overview,
[Data Mapping Overview]
質問 # 107
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