Salesforce Certified Data Cloud Consultant Practice Test - Questions Answers, Page 6

To ensure that freshly imported data from an Amazon S3 Bucket is ready and available to use for any segment, the following processes should be run in this order:

Refresh Data Stream: This process updates the data lake objects in Data Cloud with the latest data from the source system.It can be configured to run automatically or manually, depending on the data stream settings1. Refreshing the data stream ensures that Data Cloud has the most recent and accurate data from the Amazon S3 Bucket.

Identity Resolution: This process creates unified individual profiles by matching and consolidating source profiles from different data streams based on the identity resolution ruleset.It runs daily by default, but can be triggered manually as well2. Identity resolution ensures that Data Cloud has a single view of each customer across different data sources.

Calculated Insight: This process performs calculations on data lake objects or CRM data and returns a result as a new data object.It can be used to create metrics or measures for segmentation or analysis purposes3. Calculated insights ensure that Data Cloud has the derived data that can be used for personalization or activation.

1:Configure Data Stream Refresh and Frequency - Salesforce

2:Identity Resolution Ruleset Processing Results - Salesforce

3:Calculated Insights - Salesforce

The best option that the consultant should do to ensure the ecommerce data is ready for use for each of the scheduled activations is A. Use Flow to trigger a change data event on the ecommerce data to refresh calculated insights and segments before the activations are scheduled to run. This option allows the consultant to use the Flow feature of Data Cloud, which enables automation and orchestration of data processing tasks based on events or schedules. Flow can be used to trigger a change data event on the ecommerce data, which is a type of event that indicates that the data has been updated or changed. This event can then trigger the refresh of the calculated insights and segments that depend on the ecommerce data, ensuring that they reflect the latest data. The refresh of the calculated insights and segments can be completed before the activations are scheduled to run, ensuring that the customer's scheduled campaign messages are accurate and relevant.

The other options are not as good as option A. Option B is incorrect because setting a refresh schedule for the calculated insights to occur every hour may not be sufficient or efficient. The refresh schedule may not align with the activation schedule, resulting in outdated or inconsistent data. The refresh schedule may also consume more resources and time than necessary, as the ecommerce data may not change every hour. Option C is incorrect because ensuring the activations are set to Incremental Activation and automatically publish every hour may not solve the problem. Incremental Activation is a feature that allows only the new or changed records in a segment to be activated, reducing the activation time and size. However, this feature does not ensure that the segment data is updated or refreshed based on the ecommerce data. The activation schedule may also not match the ecommerce data update schedule, resulting in inaccurate or irrelevant campaign messages. Option D is incorrect because ensuring the segments are set to Rapid Publish and set to refresh every hour may not be optimal or effective. Rapid Publish is a feature that allows segments to be published faster by skipping some validation steps, such as checking for duplicate records or invalid values. However, this feature may compromise the quality or accuracy of the segment data, and may not be suitable for all use cases. The refresh schedule may also have the same issues as option B, as it may not sync with the ecommerce data update schedule or the activation schedule, resulting in outdated or inconsistent data.

A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:

The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes.

The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.

The Data Rights Subject Request tool is a feature that allows Data Cloud users to manage customer requests for data access, deletion, or portability. The tool provides a user interface and an API to create, track, and fulfill data rights requests. The tool also generates a report that contains the customer's personal data and the actions taken to comply with the request. The consultant should use this tool to accommodate the customer's request for data deletion in Data Cloud.

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

: NTO is using Fuzzy Name and Normalized Email as match rules to link together data from different sources into a unified individual profile. However, there might be cases where the same email address is available from more than one source, and NTO needs to decide which one to use for activation. For example, if Rachel has the same email address in Service Cloud and Marketing Cloud, but prefers to receive communications from NTO via Marketing Cloud, NTO needs to ensure that the email address from Marketing Cloud is activated. To do this, NTO can use the source priority order in activations, which allows them to rank the data sources in order of preference for activation. By placing Marketing Cloud higher than Service Cloud in the source priority order, NTO can make sure that the email address from Marketing Cloud is delivered to the activation target, such as an email campaign or a journey. This way, NTO can respect Rachel's preference and deliver a better customer experience.

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.

Identity resolution is the feature that allows Data Cloud to match and reconcile data about individuals from multiple data sources into a single unified profile. Identity resolution uses rulesets to define how source profiles are matched and consolidated based on common attributes, such as name, email, phone, or party identifier.Identity resolution enables Data Cloud to create a 360-degree view of each customer across different data sources and systems12. The other options are not the best features to highlight for this customer need because:

A . Data cleansing is the process of detecting and correcting errors or inconsistencies in data, such as duplicates, missing values, or invalid formats.Data cleansing can improve the quality and accuracy of data, but it does not match or reconcile data across different data sources3.

B . Harmonization is the process of standardizing and transforming data from different sources into a common format and structure.Harmonization can enable data integration and interoperability, but it does not match or reconcile data across different data sources4.

C . Data consolidation is the process of combining data from different sources into a single data set or system.Data consolidation can reduce data redundancy and complexity, but it does not match or reconcile data across different data sources5. Reference

The consultant should use B. Segmentation exclude rules to remove the recent customers. Segmentation exclude rules are filters that can be applied to a segment to exclude records that meet certain criteria. The consultant can use segmentation exclude rules to exclude customers who have made purchases within the last week from the segment that contains customers who have purchased within the past 6 months. This way, the segment will only include customers who are eligible for the promotional campaign.

The other options are not correct. Option A is incorrect because batch transforms are data processing tasks that can be applied to data streams or data lake objects to modify or enrich the data. Batch transforms are not used for segmentation or activation. Option C is incorrect because related attributes are attributes that are derived from the relationships between data model objects. Related attributes are not used for excluding records from a segment. Option D is incorrect because streaming insights are derived attributes that are calculated at the time of data ingestion. Streaming insights are not used for excluding records from a segment.