2025年最新の実際に出る無料Salesforce Data-Cloud-Consultant試験問題集と解答
Data-Cloud-Consultant練習テストエンジンで今すぐ試そう172試験問題
質問 # 26
How does Data Cloud handle an individual's Right to be Forgotten?
- A. Deletes the specified Individual record and its Unified Individual Link record.
- B. Deletes the specified Individual and records from any data model object/data lake object related to the Individual.
- C. Deletes the records from all data source objects, and any downstream data model objects are updated at the next scheduled ingestion
- D. Deletes the specified Individual and records from any data source object mapped to the Individual data model object.
正解：B
解説：
Data Cloud handles an individual's Right to be Forgotten by deleting the specified Individual and records from any data model object/data lake object related to the Individual. This means that Data Cloud removes all the data associated with the individual from the data space, including the data from the source objects, the unified individual profile, and any related objects. Data Cloud also deletes the Unified Individual Link record that links the individual to the source records. Data Cloud uses the Consent API to process the Right to be Forgotten requests, which are reprocessed at 30, 60, and 90 days to ensure a full deletion.
The other options are not correct descriptions of how Data Cloud handles an individual's Right to be Forgotten. Data Cloud does not delete the records from all data source objects, as this would affect the data integrity and availability of the source systems. Data Cloud also does not delete only the specified Individual record and its Unified Individual Link record, as this would leave the source records and the related records intact. Data Cloud also does not delete only the specified Individual and records from any data source object mapped to the Individual data model object, as this would leave the related records intact.
References:
* Requesting Data Deletion or Right to Be Forgotten
* Data Deletion for Data Cloud
* Use the Consent API with Data Cloud
* Data and Identity in Data Cloud
質問 # 27
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?
- A. The team member in the Eastern time zone.
- B. Both team members; Data Cloud adjusts the segment and activation schedules to the time zone of the logged-in user
- C. Neither team member; Data Cloud shows all schedules in GMT.
- D. The team member in the Pacific time zone.
正解：B
解説：
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. References:
* 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
質問 # 28
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 'red shoes' as a single line item in a purchase
- B. Individuals who purchased at least one of any 'red' product or 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 of any red' product and also purchased at least one pair of 'shoes'
正解：D
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 # 29
A customer creates a large segment of customers that placed orders in the last 30 days, and adds related attributes from the... to the activation. Upon checking the activation in Marketing Cloud, they notice It contains orders that are older than 30 days.
What should a consultant do to resolve this issue?
- A. Apply a data space fitter to exclude orders older than 30 days.
- B. Use SQL in Marketing Cloud Engagement to remove orders older than 30 days.
- C. Apply a filter to Purchase Order Date to exclude orders older than 30 days.
- D. use data graphs that contain only 30 days of data.
正解：C
質問 # 30
A company is seeking advice from a consultant on how to address the challenge of having multiple leads and contacts in Salesforce that share the same email address. The consultant wants to provide a detailed and comprehensive explanation on how Data Cloud can be leveraged to effectively solve this issue.
What should the consultant highlight to address this company's business challenge?
- A. Identity Resolution
- B. Data Bundles
- C. Calculated Insights
- D. Identity Resolution
正解：D
解説：
Issue Overview: When multiple leads and contacts share the same email address in Salesforce, it can lead to data duplication, inaccurate customer views, and inefficient marketing and sales efforts.
Data Cloud Identity Resolution: Salesforce Data Cloud offers Identity Resolution as a powerful tool to address this issue. It helps in merging and unifying data from multiple sources to create a single, comprehensive customer profile.
Process:
* Data Ingestion: Import lead and contact data into Salesforce Data Cloud.
* Identity Resolution Rules: Configure Identity Resolution rules to match and merge records based on key identifiers like email addresses.
* Unification: The tool consolidates records that share the same email address, eliminating duplicates and ensuring a single view of each customer.
* Continuous Updates: As new data comes in, Identity Resolution continuously updates and maintains the unified profiles.
Benefits:
* Accurate Customer View: Reduces duplicate records and provides a complete view of each customer's interactions and history.
* Improved Efficiency: Streamlines marketing and sales efforts by targeting a unified customer profile.
References:
* Salesforce Data Cloud Identity Resolution
* Salesforce Help: Identity Resolution Overview
質問 # 31
Which two dependencies prevent a data stream from being deleted?
Choose 2 answers
- A. The underlying data lake object is mapped to a data model object.
- B. The underlying data lake object is used in segmentation.
- C. The underlying data lake object is used in a data transform.
- D. The underlying data lake object is used in activation.
正解：A、C
解説：
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.
References:
* 1: Delete a Data Stream article on Salesforce Help
* 2: [Data Transforms in Data Cloud] unit on Trailhead
* 3: [Data Model in Data Cloud] unit on Trailhead
質問 # 32
Northern Trail Outfitters (NTD) creates a calculated insight to compute recency, frequency, monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores that it activates to a Marketing Cloud activation target.
Which two actions are required when configuring the activation?
Choose 2 answers
- A. Choose a segment.
- B. Add additional attributes.
- C. Select contact points.
- D. Add the calculated insight in the activation.
正解：A、C
解説：
To configure an activation to a Marketing Cloud activation target, you need to choose a segment and select contact points. Choosing a segment allows you to specify which unified individuals you want to activate. Selecting contact points allows you to map the attributes from the segment to the fields in the Marketing Cloud data extension. You do not need to add additional attributes or add the calculated insight in the activation, as these are already part of the segment definition. Reference: Create a Marketing Cloud Activation Target; Types of Data Targets in Data Cloud
質問 # 33
A segment fails to refresh with the error "Segment references too many data lake objects (DLOS)".
Which two troubleshooting tips should help remedy this issue?
Choose 2 answers
- A. Refine segmentation criteria to limit up to five custom data model objects (DMOs).
- B. Space out the segment schedules to reduce DLO load.
- C. Use calculated insights in order to reduce the complexity of the segmentation query.
- D. Split the segment into smaller segments.
正解：C、D
解説：
The error "Segment references too many data lake objects (DLOs)" occurs when a segment query exceeds the limit of 50 DLOs that can be referenced in a single query. This can happen when the segment has too many filters, nested segments, or exclusion criteria that involve different DLOs. To remedy this issue, the consultant can try the following troubleshooting tips:
Split the segment into smaller segments. The consultant can divide the segment into multiple segments that have fewer filters, nested segments, or exclusion criteria. This can reduce the number of DLOs that are referenced in each segment query and avoid the error. The consultant can then use the smaller segments as nested segments in a larger segment, or activate them separately.
Use calculated insights in order to reduce the complexity of the segmentation query. The consultant can create calculated insights that are derived from existing data using formulas. Calculated insights can simplify the segmentation query by replacing multiple filters or nested segments with a single attribute. For example, instead of using multiple filters to segment individuals based on their purchase history, the consultant can create a calculated insight that calculates the lifetime value of each individual and use that as a filter.
The other options are not troubleshooting tips that can help remedy this issue. Refining segmentation criteria to limit up to five custom data model objects (DMOs) is not a valid option, as the limit of 50 DLOs applies to both standard and custom DMOs. Spacing out the segment schedules to reduce DLO load is not a valid option, as the error is not related to the DLO load, but to the segment query complexity.
References:
Troubleshoot Segment Errors
Create a Calculated Insight
Create a Segment in Data Cloud
質問 # 34
Which data stream category should be assigned to use the data for time-based operations in segmentation and calculated insights?
- A. Engagement
- B. Individual
- C. Sales Order
- D. Transaction
正解：D
解説：
Data streams are the sources of data that are ingested into Data Cloud and mapped to the data model. Data streams have different categories that determine how the data is processed and used in Data Cloud. Transaction data streams are used for time-based operations in segmentation and calculated insights, such as filtering by date range, aggregating by time period, or calculating time-to-event metrics. Transaction data streams are typically used for event data, such as purchases, clicks, or visits, that have a timestamp and a value associated with them. Reference: Data Streams, Data Stream Categories
質問 # 35
A consultant is connecting sales order data to Data Cloud and considers whether to use the Profile, Engagement, or Other categories to map the DLO. The consultant chooses to map the DLO called Order-Headers to the Sales Order DMO using the Engagement category.
What is the impact of this action on future mappings?
- A. When mapping a Profile DLO to the Sales Order DMO, the category gets updated to Profile.
- B. Only Engagement category DLOs can be mapped to the Sales Order DMO. Sales Order gets assigned to the Engagement Category.
- C. A DLO with category Engagement can be mapped to any DMO using either Profile. Engagement, or Other categories.
- D. Sales Order DMO gets assigned to both the Profile and Engagement categories when mapping a Profile DLO.
正解：B
解説：
* Data Lake Objects (DLOs) and Data Model Objects (DMOs): In Salesforce Data Cloud, DLOs are mapped to DMOs to organize and structure data. Categories like Profile, Engagement, and Other define how these mappings are used.
* Engagement Category: Mapping a DLO to the Engagement category indicates that the data is related to customer interactions and activities.
* Impact on Future Mappings:
Engagement Category Restriction: When a DLO like Order-Headers is mapped to the Sales Order DMO under the Engagement category, future mappings of the Sales Order DMO are restricted to Engagement category DLOs.
Category Assignment: The Sales Order DMO is assigned to the Engagement category, meaning only DLOs categorized as Engagement can be mapped to it in the future.
* Benefits:
Consistency: Ensures consistent data categorization and usage, aligning data with its intended purpose.
Accuracy: Helps in maintaining the integrity of data mapping and ensures that engagement-related data is accurately captured and utilized.
* Reference:
Salesforce Data Cloud Mapping
Salesforce Data Cloud Categories
質問 # 36
A customer wants to create segments of users based on their Customer Lifetime Value.
However, the source data that will be brought into Data Cloud does not include that key performance indicator (KPI).
Which sequence of steps should the consultant follow to achieve this requirement?
- A. Ingest Data > Create Calculated Insight > Map Data to Data Model > Use in Segmentation
- B. Create Calculated Insight > Ingest Data > Map Data to Data Model> Use in Segmentation
- C. Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation
- D. Create Calculated Insight > Map Data to Data Model> Ingest Data > Use in Segmentation
正解：C
解説：
To create segments of users based on their Customer Lifetime Value (CLV), the sequence of steps that the consultant should follow is Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation. This is because the first step is to ingest the source data into Data Cloud using data streams1. The second step is to map the source data to the data model, which defines the structure and attributes of the data2. The third step is to create a calculated insight, which is a derived attribute that is computed based on the source or unified data3. In this case, the calculated insight would be the CLV, which can be calculated using a formula or a query based on the sales order data4. The fourth step is to use the calculated insight in segmentation, which is the process of creating groups of individuals or entities based on their attributes and behaviors. By using the CLV calculated insight, the consultant can segment the users by their predicted revenue from the lifespan of their relationship with the brand. The other options are incorrect because they do not follow the correct sequence of steps to achieve the requirement. Option B is incorrect because it is not possible to create a calculated insight before ingesting and mapping the data, as the calculated insight depends on the data model objects3. Option C is incorrect because it is not possible to create a calculated insight before mapping the data, as the calculated insight depends on the data model objects3. Option D is incorrect because it is not recommended to create a calculated insight before mapping the data, as the calculated insight may not reflect the correct data model structure and attributes3. References: Data Streams Overview, Data Model Objects Overview, Calculated Insights Overview, Calculating Customer Lifetime Value (CLV) With Salesforce, [Segmentation Overview]
質問 # 37
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. Data stream attributes
- B. Direct attributes
- C. Related attributes
- D. Streaming insights
- E. Calculated Insights
正解：B、C、E
解説：
Explanation
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 # 38
A new user of Data Cloud only needs to be able to review individual rows of ingested data and validate that it has been modeled successfully to its linked data model object. The user will also need to make changes if required.
What is the minimum permission set needed to accommodate this use case?
- A. Data Cloud for Marketing Data Aware Specialist
- B. Data Cloud for Marketing Specialist
- C. Data Cloud Admin
- D. Data Cloud User
正解：D
解説：
The Data Cloud User permission set is the minimum permission set needed to accommodate this use case. The Data Cloud User permission set grants access to the Data Explorer feature, which allows the user to review individual rows of ingested data and validate that it has been modeled successfully to its linked data model object. The user can also make changes to the data model object fields, such as adding or removing fields, changing field types, or creating formula fields. The Data Cloud User permission set does not grant access to other Data Cloud features or tasks, such as creating data streams, creating segments, creating activations, or managing users. The other permission sets are either too restrictive or too permissive for this use case. The Data Cloud for Marketing Specialist permission set only grants access to the segmentation and activation features, but not to the Data Explorer feature. The Data Cloud Admin permission set grants access to all Data Cloud features and tasks, including the Data Explorer feature, but it is more than what the user needs. The Data Cloud for Marketing Data Aware Specialist permission set grants access to the Data Explorer feature, but also to the segmentation and activation features, which are not required for this use case. Reference: Data Cloud Standard Permission Sets, Data Explorer, Set Up Data Cloud Unit
質問 # 39
Cumulus Financial wants to create a segment of individuals based on transaction history dat a. This data has been mapped in the data model and is accessible via multiple container paths for segmentation.
What happens if the optimal container path for this use case is not selected?
- A. The resulting segment will not be generated.
- B. Data Cloud segmentation will automatically select the optimal container path.
- C. The resulting segment may be smaller or larger than expected.
- D. Alternate container paths will be suggested before the segment is published.
正解：C
解説：
In Salesforce Data Cloud, when segmenting individuals based on transaction history data, there may be multiple paths to the same data through different objects in the data model. If the wrong container path is selected:
The segment may pull in too many or too few individuals because different container paths may define relationships differently.
Some records may be unintentionally excluded or duplicated, affecting segmentation accuracy.
Identity resolution and relationships between objects might not behave as expected.
Why Not A? Data Cloud does not suggest alternate container paths automatically. The user must choose the correct path.
Why Not C? Data Cloud does not automatically select the optimal path; it relies on the user's selection.
Why Not D? The segment will still be generated but may have inaccurate results.
Salesforce Data Cloud Reference:
Salesforce Help Documentation - Data Model and Segmentation Best Practices Trailhead Module: Segmentation in Data Cloud Salesforce Knowledge Base - Using Container Paths for Segmentation
質問 # 40
Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use.
Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?
- A. Create a High Investment Balance calculated insight.
- B. Create new segments using nested segments.
- C. Package High Investment Balance Customers in a data kit.
- D. Create new segments by cloning High Investment Balance Customers.
正解：B
解説：
Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:
B . A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.
C . A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.
D . Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy. Reference: Create a Nested Segment - Salesforce, Save Time with Nested Segments (Generally Available) - Salesforce, Calculated Insights - Salesforce, Create and Publish a Data Kit Unit | Salesforce Trailhead, Create a Segment in Data Cloud - Salesforce
質問 # 41
Which permission setting should a consultant check if the custom Salesforce CRM object is not available in New Data Stream configuration?
- A. Confirm the Create object permission is enabled in the Data Cloud org.
- B. Confirm that the Modify Object permission is enabled in the Data Cloud org.
- C. Confirm the View All object permission is enabled in the source Salesforce CRM org.
- D. Confirm the Ingest Object permission is enabled in the Salesforce CRM org.
正解：C
解説：
Explanation
To create a new data stream from a custom Salesforce CRM object, the consultant needs to confirm that the View All object permission is enabled in the source Salesforce CRM org. This permission allows the user to view all records associated with the object, regardless of sharing settings1. Without this permission, the custom object will not be available in the New Data Stream configuration2. References:
* Manage Access with Data Cloud Permission Sets
* Object Permissions
質問 # 42
A customer needs to integrate in real time with Salesforce CRM.
Which feature accomplishes this requirement?
- A. Streaming transforms
- B. Sales and Service bundle
- C. Data actions and Lightning web components
- D. Data model triggers
正解：A
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
* : [Data Model in Data Cloud] unit on Trailhead
* : [Create a Data Model Object] article on Salesforce Help
* : [Data Sources in Data Cloud] unit on Trailhead
* : [Connect and Ingest Data in Data Cloud] article on Salesforce Help
* : [Data Spaces in Data Cloud] unit on Trailhead
* : [Create a Data Space] article on Salesforce Help
* : [Segments in Data Cloud] unit on Trailhead
* : [Create a Segment] article on Salesforce Help
* : [Activations in Data Cloud] unit on Trailhead
* : [Create an Activation] article on Salesforce Help
質問 # 43
During a privacy law discussion with a customer, the customer indicates they need to honor requests for the right to be forgotten. The consultant determines that Consent API will solve this business need.
Which two considerations should the consultant inform the customer about?
Choose 2 answers
- A. Data deletion requests are processed within 1 hour.
- B. Data deletion requests submitted to Data Cloud are passed to all connected Salesforce clouds.
- C. Data deletion requests are reprocessed at 30, 60, and 90 days.
- D. Data deletion requests are submitted for Individual profiles.
正解：B、D
解説：
Explanation
When advising a customer about using the Consent API in Salesforce to comply with requests for the right to be forgotten, the consultant should focus on two primary considerations:
* Data deletion requests are submitted for Individual profiles (Answer C): The Consent API in Salesforce is designed to handle data deletion requests specifically for individual profiles. This means that when a request is made to delete data, it is targeted at the personal data associated with an individual's profile in the Salesforce system. The consultant should inform the customer that the requests must be specific to individual profiles to ensure accurate processing and compliance with privacy laws.
* Data deletion requests submitted to Data Cloud are passed to all connected Salesforce clouds (Answer D): When a data deletion request is made through the Consent API in Salesforce Data Cloud, the request is not limited to the Data Cloud alone. Instead, it propagates through all connected Salesforce clouds, such as Sales Cloud, Service Cloud, Marketing Cloud, etc. This ensures comprehensive compliance with the right to be forgotten across the entire Salesforce ecosystem. The customer should be aware that the deletion request will affect all instances of the individual's data across the connected Salesforce environments.
質問 # 44
A finance company that uses Data Cloud wants to simplify how its users can view all the various channels a customer engages with Which feature should the consultant recommend to meet this requirement?
- A. Create segments based on the ingested data and insights to activate in Marketing Cloud.
- B. Use Data Cloud to connect with analytic tools, like Tableau.
- C. Use Data Cloud to ingest data from various available data sources.
- D. Use calculated insights to determine when and how to engage with various customers.
正解：C
質問 # 45
A consultant at Northern Trail Outfitters is attempting to ingest a field from the Contact object in Salesforce CRM that contains both yyyy-mm-dd and yyyy-mm-dd hh:mm:ss values. The target field is set to Date datatype.
Which statement is true in this situation?
- A. The target field will be able to hold both types of values.
- B. The target field will only hold the time part and ignore the date part.
- C. The target field will only hold the date part and ignore the time part.
- D. The target field will throw an error and store null values.
正解：C
解説：
* Field Data Types: Salesforce CRM's Contact object fields can store data in various formats. When ingesting data into Salesforce Data Cloud, the target field's data type determines how the data is processed and stored.
* Date Data Type: If the target field in Data Cloud is set to Date data type, it is designed to store date values without time information.
* Mixed Format Values: When ingesting a field containing both date (yyyy-mm-dd) and datetime (yyyy-mm-dd hh:mm:ss) values into a Date data type field:
The Date field will extract and store only the date part (yyyy-mm-dd), ignoring the time part (hh:mm:ss).
* Result:
Date Values: yyyy-mm-dd values are stored as-is.
Datetime Values: yyyy-mm-dd hh:mm:ss values are truncated to yyyy-mm-dd, and the time component is ignored.
* Reference:
Salesforce Data Cloud Field Mapping
Salesforce Data Types
質問 # 46
Cumulus Financial is currently using Data Cloud and ingesting transactional data from its backend system via an S3 Connector in upsert mode. During the initial setup six months ago, the company created a formula field in Data Cloud to create a custom classification. It now needs to update this formula to account for more classifications.
What should the consultant keep in mind with regard to formula field updates when using the S3 Connector?
- A. Data Cloud will initiate a full refresh of data from $3 and will update the formula on all records.
- B. Data Cloud will only update the formula on a go-forward basis for new records.
- C. Data Cloud does not support formula field updates for data streams of type upsert.
- D. Data Cloud will update the formula for all records at the next incremental upsert refresh.
正解：D
解説：
A formula field is a field that calculates a value based on other fields or constants. When using the S3 Connector to ingest data from an Amazon S3 bucket, Data Cloud supports creating and updating formula fields on the data lake objects (DLOs) that store the data from the S3 source. However, the formula field updates are not applied immediately, but rather at the next incremental upsert refresh of the data stream. An incremental upsert refresh is a process that adds new records and updates existing records from the S3 source to the DLO based on the primary key field. Therefore, the consultant should keep in mind that the formula field updates will affect both new and existing records, but only after the next incremental upsert refresh of the data stream. The other options are incorrect because Data Cloud does not initiate a full refresh of data from S3, does not update the formula only for new records, and does support formula field updates for data streams of type upsert. References: Create a Formula Field, Amazon S3 Connection, Data Lake Object
質問 # 47
Cumulus Financial is currently using Data Cloud and ingesting transactional data from its backend system via an S3 Connector in upsert mode. During the initial setup six months ago, the company created a formula field in Data Cloud to create a custom classification. It now needs to update this formula to account for more classifications.
What should the consultant keep in mind with regard to formula field updates when using the S3 Connector?
- A. Data Cloud will initiate a full refresh of data from $3 and will update the formula on all records.
- B. Data Cloud will only update the formula on a go-forward basis for new records.
- C. Data Cloud does not support formula field updates for data streams of type upsert.
- D. Data Cloud will update the formula for all records at the next incremental upsert refresh.
正解：D
解説：
Explanation
A formula field is a field that calculates a value based on other fields or constants. When using the S3 Connector to ingest data from an Amazon S3 bucket, Data Cloud supports creating and updating formula fields on the data lake objects (DLOs) that store the data from the S3 source.However, the formula field updates are not applied immediately, but rather at the next incremental upsert refresh of the data stream. An incremental upsert refresh is a process that adds new records and updates existing records from the S3 source to the DLO based on the primary key field. Therefore, the consultant should keep in mind that the formula field updates will affect both new and existing records, but only after the next incremental upsert refresh of the data stream. The other options are incorrect because Data Cloud does not initiate a full refresh of data from S3, does not update the formula only for new records, and does support formula field updates for data streams of type upsert. References: Create a Formula Field, Amazon S3 Connection, Data Lake Object
質問 # 48
Cumulus Financial needs to create a composite key on an incoming data source that combines the fields Customer Region and Customer Identifier.
Which formula function should a consultant use to create a composite key when a primary key is not available in a data stream?
- A. COALE
- B. COMBIN
- C. CAST
- D. CONCAT
正解：D
解説：
Composite Keys in Data Streams: When working with data streams in Salesforce Data Cloud, there may be situations where a primary key is not available. In such cases, creating a composite key from multiple fields ensures unique identification of records.
Formula Functions: Salesforce provides several formula functions to manipulate and combine data fields.
Among them, the CONCAT function is used to combine multiple strings into one.
Creating Composite Keys: To create a composite key using CONCAT, a consultant can combine the values of Customer Region and Customer Identifier into a single unique identifier.
* Example Formula: CONCAT(Customer_Region, Customer_Identifier)
References:
* Salesforce Documentation: Formula Functions
* Salesforce Data Cloud Guide
質問 # 49
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