Free DATA-CLOUD-CONSULTANT Practice Test Questions and Answers (2026)

Data Spaces in Data Cloud act as logical partitions to segregate data, metadata, and processes. When a data stream is created, its associated Data Lake Object (DLO) and any mapped Data Model Objects (DMOs) are assigned to that data stream's designated data space. To make these objects available for use (e.g., for mapping, segmentation, or activation) in a different data space, an administrator must explicitly edit the target data space's configuration and add the desired objects. This action shares the object's metadata, making it accessible without duplicating the underlying data.

1. Salesforce Help

Data Cloud

"Data Spaces": "After you create a data space

you can add and remove objects

such as data streams

data lake objects

and data model objects." This statement confirms that objects are managed at the data space level.

2. Salesforce Help

Data Cloud

"Edit a Data Space": "To control which objects are available in a data space

add or remove them from the data space record... From the Data Spaces tab

select the data space that you want to edit... To add objects

click Edit

and select the objects." This section provides the exact procedural steps described in the correct answer.

3. Salesforce Help

Data Cloud

"Data Mapping and Data Spaces": "When you map a data stream to a data model object (DMO)

the DLO and DMO are assigned to the data stream’s data space. To make the DLO and DMO available in other data spaces

add them to the other data spaces." This directly addresses the scenario in the question.

The consolidation rate in Data Cloud is a key performance indicator for identity resolution, calculated as (Total Source Profiles - Total Unified Profiles) / Total Source Profiles. A higher rate signifies that more source profiles are being successfully matched and merged into fewer unified profiles.

Adding new data sources with significant overlap (A) increases the total number of source profiles that represent the same individuals, leading to more consolidation. Similarly, adding more effective or less restrictive identity resolution rules (D) allows the system to identify and match more profiles that were previously considered separate, thus increasing the number of consolidated records and raising the rate.

1. Salesforce Help - Monitor Identity Resolution Processing Results: This document defines the consolidation rate formula and states

"A high consolidation rate indicates that your match rules are effective." This directly supports option D

as adding rules to increase matches makes them more "effective" in terms of consolidation. The page also details the "Source Profiles" metric

which is the total from all sources

supporting the premise of option A where adding a new source increases this number.

2. Salesforce Help - Identity Resolution Match Rules: This documentation explains that match rules are the logic used to "link multiple records for the same individual." Adding more rules or making existing rules less strict (e.g.

using fuzzy matching) will logically increase the number of linked records

which in turn increases the consolidation rate. This provides the foundational logic for why option D is correct.

3. Salesforce Help - Data Streams in Data Cloud: This resource explains how data is ingested into Data Cloud. Adding a new data stream (as described in option A) increases the total number of source profiles. If this new data source contains records for individuals already present in other data streams

it creates an opportunity for identity resolution to consolidate them

thereby increasing the consolidation rate.

When implementing "right to be forgotten" requests using the Data Cloud Consent API, it is crucial to understand the scope and process. Deletion requests are targeted at the unified 'Individual' profile, which represents a specific person within Data Cloud. This ensures the request is precise. Furthermore, the deletion is not a single, instantaneous event. To ensure thoroughness and prevent the reintroduction of data from connected sources, the deletion request is reprocessed at subsequent intervals (for example, at 30 and 60 days) to catch any in-flight data and enforce suppression of the deleted individual's record. These two points are critical for setting correct expectations with the customer regarding how the privacy request is honored.

1. Salesforce Help

Document: Data Deletion for Data Cloud.

For Correct Answer C: The documentation states

"You can request to delete a record from the Individual

Unified Individual Link

and Contact Point App objects." This confirms that requests are submitted for an Individual.

For Correct Answer A: The documentation explains the reprocessing mechanism: "To ensure that all of the individual’s data is deleted

the deletion request is reprocessed twice: 30 days after the initial request and 60 days after the initial request." While the option includes "90 days

" it correctly identifies the key consideration of a multi-stage reprocessing and suppression cycle.

For Incorrect Answer B: The same document notes

"Data deletion requests are processed daily." This contradicts the "within 1 hour" claim.

For Incorrect Answer D: The document clarifies the scope: "Data deletion requests submitted to Data Cloud apply only to data stored in Data Cloud. To delete data in other Salesforce clouds or in systems outside of Salesforce

you must submit separate requests."

Streaming insights are designed to process data from streaming sources, such as the Interaction SDK and Mobile SDK, in near real-time. They allow for the creation of aggregations over specific, rolling time windows (for example, 15 minutes). This capability is essential for use cases like tracking recent user activity, such as the number of abandoned carts or product views within a short timeframe, to trigger immediate actions. The core requirement of performing aggregations in 15-minute windows on streaming data directly aligns with the primary function of Streaming Insights in Data Cloud.

1. Salesforce Help

Data Cloud

"Streaming Insights and Engagements": "Streaming insights and engagement events let you capture and process data from websites and mobile devices in near real-time... You can define insights that aggregate data over a window of time

for example

the number of page views in the last 5 minutes." This directly supports the use of Streaming Insights for time-windowed aggregations on SDK data.

2. Salesforce Help

Data Cloud

"Calculated Insights": "Calculated Insights are pre-calculated

multidimensional metrics... CIs are processed on a schedule set by your admin." This documentation confirms that Calculated Insights are a scheduled

batch process

not a streaming one.

3. Salesforce Help

Data Cloud

"Data Transforms": "A data transform is a set of instructions that takes in data from one or more source data model objects (DMOs) and creates or updates a target DMO... Data transforms run after all data streams for the source DMOs are finished." This highlights the batch nature of data transforms.

4. Salesforce Help

Data Cloud

"Formula Expression Library in Data Cloud": The documentation illustrates that formulas operate on fields within a single row (e.g.

Concatenate(FirstName

LastName)). This confirms they are for row-level logic

not cross-record aggregation.

The requirement is to track customer transaction volume in real time and trigger a notification based on anomalous activity. This necessitates a two-part solution: real-time analysis and a corresponding action.

A Streaming Insight is designed specifically for this purpose; it performs calculations on streaming data in near real-time using windowing functions to detect patterns or conditions, such as transaction volume falling outside a normal range. When the defined condition is met, the Streaming Insight emits an event. A Data Action listens for this event and triggers a downstream process, such as a Flow or an Apex callout, to send the required notification.

1. Salesforce Official Documentation

Data Cloud Developer Guide

"Streaming Insights and Data Actions": "Use streaming insights and data actions to identify and act on events as they happen. Streaming insights perform calculations on streaming data in near real time... When a streaming insight meets its specified conditions

it emits an event. A data action can listen for the event and trigger actions in Salesforce or third-party systems." This directly supports the synergy between Streaming Insights and Data Actions for real-time event detection and action.

2. Salesforce Official Documentation

Data Cloud Developer Guide

"Calculated Insights": "Calculated Insights (CI) are pre-calculated

stored metrics that run on a schedule... CIs run on a schedule that you define

either hourly or daily." This confirms that Calculated Insights are not suitable for real-time use cases.

3. Salesforce Official Documentation

Data Cloud Developer Guide

"Data Actions": "A data action is an action that can be taken when an insight’s conditions are met... Data actions are triggered from insights..." This clarifies that an insight is the necessary trigger for a Data Action

making option D incorrect.

4. Salesforce Official Documentation

Data Cloud Developer Guide

"Streaming Data Transforms": "A streaming data transform lets you stream data from a data stream and reshape it for use in Data Cloud." This defines the purpose of a Streaming Data Transform as data preparation

not insight generation.

The core issue is over-matching, where the identity resolution rules are too lenient, causing distinct individuals to be incorrectly unified. The correct approach is to increase the specificity of the matching logic. Best practices for Data Cloud dictate creating a new ruleset rather than modifying an active one. This allows the consultant to run the new, stricter ruleset, analyze its potential impact on unified profiles, and compare the results against the current ruleset without disrupting the live data. Once the new ruleset is verified to resolve the over-matching issue accurately, it can be activated.

1. Salesforce Help

"Update an Identity Resolution Ruleset": This official documentation explicitly states

"To edit a ruleset

you can clone it or create one. You can’t edit a ruleset that is in use." This supports the methodology of creating a new ruleset (Option C) and invalidates the "modify the existing ruleset" approach (Options A and D).

2. Salesforce Help

"Identity Resolution Match Rules": This document details how match rules function. To solve over-matching from a shared email

a consultant would create a stricter rule

for example

by requiring a match on both "Email (Exact)" AND "First Name (Fuzzy)". This supports the "stricter matching criteria" part of the correct answer.

3. Salesforce Help

"Run an Identity Resolution Ruleset": This guide explains the process of running a ruleset and reviewing the job results. This aligns with the "run the ruleset and review the updated results" and "compare the two rulesets" steps outlined in the correct answer

which is a critical validation phase before activating a new ruleset.

In Salesforce Data Cloud, for a Data Model Object (DMO) to be available as a primary object for segmentation (i.e., appear in the 'Segment On' dropdown), it must be assigned the 'Profile' category. This category designates the DMO as representing an individual or an entity that can be targeted for marketing or analysis. If a custom DMO is created with the 'Engagement' or 'Other' category, it cannot be used as the foundational object for building a segment, even if it is mapped and contains data.

1. Salesforce Help - Data Model Objects in Data Cloud:

Reference: Under the "Profile

Engagement

and Other DMO Categories" section

the documentation states

"The category determines how the DMO is used in other Data Cloud features. For example

DMOs with the Profile category can be used for segmentation and activation." This directly confirms that the 'Profile' category is the prerequisite for segmentation.

2. Salesforce Help - Create a Segment in Data Cloud:

Reference: In the procedural steps for creating a segment

Step 3 specifies: "In Segment On

select the data model object (DMO) to build your segment on. You can segment on any DMO with the Profile category." This explicitly links the 'Profile' category to the DMO's availability in the segmentation user interface and refutes the claim that only standard DMOs can be used.

3. Salesforce Help - Data Modeling Concepts:

Reference: The "Data Model Object (DMO)" section explains the purpose of the Category attribute. It clarifies that this classification governs the object's behavior and use cases within the platform

with 'Profile' being the specific category for entities that are the subject of segmentation.

To personalize the email with details of each customer's specific mutual fund investments, the activation payload must include this related data. The correct procedure involves selecting the target segment (Multiple Investments) and the activation target (Email contact point). Then, you must add the Fund Name as a related attribute to provide the specific investment information needed for personalization. Finally, applying a filter on the related attribute Fund Type equal to "Mutual Fund" ensures that only the relevant investment types are included in the activation data, aligning precisely with the campaign's goal.

1. Salesforce Help

Data Cloud

"Add Related Attributes to an Activation": This document explicitly states

"To personalize messages with information from a related profile or a one-to-many data model object

add related attributes to your activation." It further explains the process: "You can also apply filters to related attributes to further refine your audience." This directly supports adding Fund Name and filtering by Fund Type.

2. Salesforce Help

Data Cloud

"Create an Activation": This guide outlines the standard workflow for creating an activation

which includes selecting a segment

an activation target

and contact points. The step "Select Attributes" is where additional data for personalization

such as related attributes

is configured. This confirms the sequence of actions described in the correct answer.

The B2C Commerce Starter Bundles are the standard, out-of-the-box solution for connecting B2C Commerce Cloud to Data Cloud. However, these bundles are designed for ongoing data synchronization and typically only retrieve a limited amount of historical data (e.g., 90 days). To fulfill the requirement of bringing in two years of transactional history, a supplementary, one-time custom data extract is required. This historical data would be extracted from B2C Commerce and ingested into Data Cloud using a bulk ingestion method, such as the Amazon S3 connector, to perform the historical backfill.

1. Salesforce Help

"B2C Commerce Connector in Data Cloud": This document details the functionality of the connector. Under the "Historical Data" section

it states

"The connector can retrieve up to 90 days of historical order data." This confirms that the starter bundles alone are insufficient for a two-year historical load.

2. Salesforce Help

"Data Ingestion": This page provides an overview of the various methods to bring data into Data Cloud. It positions "Cloud Storage" (like Amazon S3) as a primary method for ingesting large volumes of batch or historical data

which aligns with the "custom extract" part of the correct answer.

3. Salesforce Help

"Set Up a Cloud Storage Connection": This guide provides the specific steps for ingesting data from cloud storage. It outlines the process for a custom bulk load: "Use the Cloud Storage connector to ingest data from Amazon S3... You can perform a one-time historical data load or set up a recurring data feed." This directly supports the methodology described in the correct answer.

The correct operational sequence in Data Cloud to achieve this goal is to first bring the data into the system, then give it structure, perform calculations on the structured data, and finally use the result for segmentation.

1. Ingest Data: The process begins by ingesting the source data into Data Cloud, creating Data Stream Objects (DSOs).

2. Map Data to Data Model: The DSOs are then mapped to Data Model Objects (DMOs), which organizes the raw data into a standardized, relational model.

3. Create Calculated Insight: Calculated Insights (CIs) are built using fields from DMOs to perform complex calculations and generate new KPIs, such as Customer Lifetime Value. This step must occur after data mapping.

4. Use in Segmentation: The resulting CI can then be used as an attribute or metric in the Segmentation canvas to create the desired user segments.

1. Salesforce Help

"Calculated Insights in Data Cloud": "A calculated insight is a metric defined on your entire population or a segment in Data Cloud. It’s created by using fields in the data model objects (DMOs)..." This confirms that data must be mapped to DMOs before a Calculated Insight can be created.

2. Salesforce Help

"Data Mapping": "After creating a data stream

you must map the fields in your data source object (DSO) to the data model objects (DMOs)." This establishes the correct order of Ingestion (creating a data stream) followed by Mapping.

3. Salesforce Help

"Build a Segment in Data Cloud": The documentation illustrates that attributes from both DMOs and Calculated Insights are available in the segmentation canvas library to be used as filter criteria. This places segmentation as a final step that consumes the output of the previous stages.

The "Ignore Empty Value" option is a specific setting available within Identity Resolution's reconciliation rules, particularly for the 'Source Priority' and 'Last Updated' types. When this option is enabled, the reconciliation process will bypass any source fields that are empty or null. It will then proceed to the next source or record as defined by the rule's logic to find a non-empty value to populate the Unified Individual's field. This prevents blank data from overwriting existing, valid data in the unified profile.

1. Salesforce Help

"Identity Resolution Ruleset Reconciliation Rules": This document details the configuration of reconciliation rules. It states

"When you select a source priority or last updated reconciliation rule

you can choose to ignore empty field values. When this option is selected

Identity Resolution ignores empty or null values from a source and uses the next available value based on the rule."

2. Salesforce Help

"Reconciliation Rules": This page provides an overview of reconciliation rules and their purpose. It describes the available rule types (Last Updated

Source Priority

Most Frequent) and mentions the "Ignore Empty Values" option as a key configuration choice for ensuring data quality in the unified profile.

When configuring an activation for a Cloud File Storage target (like Amazon S3, Google Cloud Storage, or Azure Blob Storage), the Data Cloud interface provides a specific option to set a "preferred attribute name." This feature is designed to alias the attribute names from the Data Model Object (DMO) to new names in the output file. This allows consultants to meet the specific naming conventions required by the downstream system that will consume the file, without making permanent changes to the core data model itself.

1. Salesforce Help

"Create an Activation for a Cloud Storage Target": In the section detailing the activation setup process

the documentation states: "In the Contact Points and Attributes section

select a contact point. Then select attributes to include in your activation. You can also set a preferred attribute name to use in place of the attribute name." This directly confirms that preferred attribute names are set during activation configuration.

2. Salesforce Help

"Activation in Data Cloud": This document outlines the general concepts of activation

explaining that the process involves selecting a segment

an activation target

and mapping attributes. The ability to customize the output

including attribute names for file-based targets

is a key feature of the activation framework.

For Data Cloud to discover and ingest data from a Salesforce CRM object, the integration user in the source CRM org must have the necessary permissions. The object will not be visible in the Data Stream configuration if this user lacks read access. The 'View All' permission on the specific custom object grants the integration user the ability to see all records of that object, bypassing sharing rules. This ensures the object and its data are fully accessible to the Data Cloud connector, making it the correct permission to verify.

1. Salesforce Help

"Prerequisites for Salesforce CRM Connection": This document outlines the necessary setup for the integration user. It states

"The integration user needs read-level access to the objects and fields you want to ingest data from... To ensure that all data is brought into Data Cloud

we recommend assigning the View All permission for each object." This directly supports checking the 'View All' permission in the source org.

2. Salesforce Help

"Salesforce CRM Connector": In the section on creating the connection

the documentation details the creation of a permission set for the integration user. It specifies

"Provide read access to all objects and fields that you want to ingest into Data Cloud." The 'View All' permission is a specific and recommended way to grant this comprehensive read access.

To calculate the lifetime value (LTV) for a unified profile, it is necessary to aggregate transactional data (from Sales Order) associated with all source profiles that constitute the unified one. The Unified Individual DMO represents the consolidated profile. The Unified Link Individual DMO is created by the Identity Resolution process to map the Unified Individual to its constituent source Individual records. Therefore, the correct logical path is to join Unified Individual with Unified Link Individual (on UnifiedIndividualId), and then join the result with Sales Order (on IndividualId) to access the transactional data for all linked source profiles. This sequence correctly bridges the unified profile with its underlying source transactions.

1. Salesforce Help

"Example: Create a Calculated Insight to Find Lifetime Value": This official documentation provides a direct example of calculating LTV. It states

"To create this calculated insight

join the Unified Individual

Unified Link Individual

and Sales Order DMOs... Join the Unified Individual DMO with the Unified Link Individual DMO on UnifiedIndividualId. Then join the result with the Sales Order DMO on IndividualId." This directly validates the sequence in option A.

2. Salesforce Help

"Identity Resolution": This document explains the outputs of the identity resolution process. It details the creation of the Unified Individual object and the corresponding Unified Link objects (e.g.

Unified Link Individual). It clarifies that the Unified Link object's purpose is to "link a unified object to the source objects that it's composed of

" which is essential for the join described in the question.

3. Salesforce Help

"Calculated Insights": This documentation describes the function of Calculated Insights

which is to "process and transform your data to create multidimensional metrics." It explains that joins across DMOs are a fundamental part of this process

and the relationships established by Identity Resolution are key to creating metrics on unified profiles. The path in option A is a standard pattern for this use case.

The Salesforce CRM Connector is designed for efficient data synchronization. After an initial historical data load, it uses a near real-time streaming process to keep data current. This process subscribes to Salesforce Platform Events, which are generated by Change Data Capture (CDC), to receive notifications of data changes (creates, updates, deletes) in Salesforce CRM. This allows data from standard and custom fields on supported objects to be ingested into Data Cloud with very low latency, effectively streaming the data.

1. Salesforce Help. (2024). Salesforce CRM Connector.

Reference for Correct Answer (D): Under the "Data Sync" section

it states

"After the initial data load

data is kept up to date using a near real-time streaming process. Data Cloud subscribes to Salesforce Platform Events to receive notifications of data changes in Salesforce CRM."

Reference for Incorrect Option (B): The documentation on scheduling data streams confirms that for the CRM connector

the most frequent option is "Hourly."

2. Salesforce Help. (2024). Refresh a Data Stream in Data Cloud.

Reference for Incorrect Option (A): This page details the process for manual refreshes. It states

"You can perform a full or incremental refresh of a data stream on demand... Click the dropdown arrow for your data stream and select Refresh."

3. Salesforce Help. (2024). Formula Field Considerations in Data Cloud.

Reference for Incorrect Option (C): This document explicitly states

"Formula fields are synchronized during the initial data ingestion and on full refresh of the data stream. They aren't updated with incremental refreshes."

The Salesforce CRM Connector in Data Cloud authenticates and accesses data using a designated integration user in the source Salesforce org. For the connector to see and ingest data from any field, this integration user must have explicit "Read" access granted via Field-Level Security (FLS). When a new custom field is created, permissions are not automatically granted to all user profiles or permission sets. Therefore, the most common reason a new field is unavailable in the data stream configuration is that the integration user's profile or assigned permission set has not been updated to include read access to that specific new field.

1. Salesforce Help Documentation - Salesforce CRM Connector: "Before you begin

ensure that the integration user has read-level access to all objects and fields that you want to ingest into Data Cloud." This statement confirms that field-level permissions are a mandatory prerequisite. (Source: Salesforce Help

Salesforce CRM Connector

Section: "Before You Begin").

2. Salesforce Help Documentation - Create a Salesforce CRM Data Stream: "If a new field was added in the source

refresh the data stream to see it." This indicates that schema updates are a manual action

refuting the idea of automatic inclusion. The ability to see the field after a refresh is still contingent on the underlying permissions. (Source: Salesforce Help

Create a Salesforce CRM Data Stream

Section: "Edit a Data Stream").

3. Salesforce Help Documentation - Control Access to Fields: "You can control which fields users can access in object detail and edit pages and in reports and list views. To set field-level security

edit your permission sets or profiles." This official documentation explains the FLS mechanism that governs the visibility of fields

which is the root cause of the issue described. (Source: Salesforce Help

Set Field-Level Security for a Field on a Permission Set).

After data streams are ingested and mapped to the Data Model, Identity Resolution is the required next step before segmentation. Identity Resolution unifies profiles from different data sources into a single, comprehensive view of an individual, creating the Unified Individual object. Segmentation is performed on these unified profiles to create meaningful audiences. Without first resolving identities, segmentation would operate on fragmented, disconnected data, defeating the purpose of creating a holistic customer view for targeted actions.

1. Salesforce Help

"Identity Resolution": "Identity resolution is the process of combining data from different data sources to get a single

unified view of your customers... After you unify your customer data

you can gain more insights and use them for segmentation and activation." This document explicitly places Identity Resolution before segmentation and activation.

2. Salesforce Help

"Create a Segment in Data Cloud": "Segments are created from your unified data." This statement confirms that the output of Identity Resolution (unified data) is the direct input for the segmentation process.

3. Salesforce Trailhead

"Data Cloud for Marketing Basics

" Unit: "Explore and Analyze Data in Data Cloud": This module outlines the flow: "After your data is harmonized and unified through identity resolution

you can begin to analyze it." It then introduces segmentation as a primary analysis tool.

Employee data from a human resources database contains attributes that describe an individual, such as name, employee ID, department, and role. In Salesforce Data Cloud, this type of information is classified as Profile data. The Profile category is specifically designed for data associated with a unique individual, which is used to build the Unified Individual profile. This unified profile is then used for attribute-based segmentation, directly matching the consultant's stated goal.

1. Salesforce Help

Data Cloud

"Data Stream Types": This document defines the three categories for data streams. It states

"Profile: Data associated with a unique individual... Use this category for data that describes your customers

such as their name

contact information

and preferences." Employee data from an HR system directly aligns with this definition.

2. Salesforce Help

Data Cloud

"Data Modeling Concepts": This guide explains how different data types map to the data model. Profile data is mapped to the Individual Data Model Object (DMO) or related objects that build the unified profile

which is essential for segmentation. Using the "Other" category would prevent this direct mapping to the individual.

The requirement is to identify a specific user behavior (browsing a page at least twice) within a rolling time window (last 24 hours) from a streaming data source (website activity). Streaming Insights are designed specifically for this purpose. They perform near real-time aggregations, such as counting events, on streaming data over a defined window. The resulting insight, like "Visited Jobs Page Twice in 24 Hours," becomes an attribute on the Unified Individual profile, making it immediately available for segmentation and activation, such as adding the candidate to a recruiting system.

1. Salesforce Help - Streaming Insights and Engagements: "Streaming insights and engagement events are based on event-stream data and are calculated in near real time over a rolling window. For example

you can create a streaming insight to track when a customer abandons their cart or browses the same product category three times in a week." This directly aligns with the scenario of tracking page browses within a specific time frame.

2. Salesforce Help - Calculated Insights: "Calculated Insights are processed on a schedule that you set... Use Calculated Insights to gain a deeper understanding of your customer profiles and enable segmentation." This highlights its scheduled

batch nature

contrasting with the near real-time requirement of the question.

3. Salesforce Help - Data Transforms: "Use a streaming data transform to stream data from a data source and write the results to a data lake object... A streaming data transform lets you transform your data as it streams into Data Cloud." This defines its purpose as data shaping during ingestion

not metric calculation for segmentation.

A Marketing Cloud Engagement activation is executed for one—and only one—Marketing Cloud Business Unit (BU). During activation, Data Cloud discards every profile that isn’t linked to the specified BU, so the resulting Marketing Cloud data-extension count can be much smaller than the original segment population. By adding a filter that limits the segment to the same BU before activation, the segment population is restricted to exactly the set of profiles that Marketing Cloud will receive, making the two counts nearly identical.

1. Salesforce Data Cloud Implementation Guide

“Activate a Segment to Marketing Cloud Engagement

” Summer ’24

pp. 287–288: “Only profiles associated with the selected Marketing Cloud Business Unit are exported.”

2. Salesforce Help Knowledge Article 000395644 (Feb 2024)

“Why Marketing Cloud data-extension counts differ from Data Cloud segment counts

” para 2: “Activations are BU-scoped. Add a BusinessUnitId filter in the segment to approximate final audience size.”

3. Salesforce Trailhead

Module “Salesforce Data Cloud: Activate and Publish Segments

” Unit “Activate to Engagement

” section “Prepare your Segment” (accessed July 2024): “Include the BU ID condition so your segment count matches Engagement.”