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| Section | Weight | Objectives |
|---|---|---|
| Analytics & Performance Insights | 8% | - Marketing data visualization and performance insights - Standard dashboards and reporting selection |
| Consent | 13% | - Consent banner configuration and collection methods - Consent management concepts and regulatory compliance - Consent data objects and records management |
| Agentforce & AI Innovation | 11% | - Marketing Agents for campaign and audience automation - Conversational messaging with Agentforce - Predictive AI capabilities and use cases |
| Campaign Design, Flow Orchestration & Content | 30% | - Activation templates, landing pages and multi-channel design - Personalization with Handlebars and dynamic content - Flow design, orchestration, triggers and messaging |
| Data Modeling, Identity Resolution & Segmentation | 25% | - Identity Resolution and unified customer profiles - Data ingestion, segmentation and audience activation - Data 360 Data Model Objects (DMO) and data relationships |
| Platform Setup & Governance | 13% | - Business Units, governance model, roles and permissions - Marketing Cloud Next environment provisioning and Data 360 setup - Domain authentication (DKIM, SPF) and IP configuration |
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NEW QUESTION # 85
A car dealership manages both retail consumers (B2C) and business fleet customers (B2B). Customer data for each audience is ingested through separate data streams in a single data space and mapped to the Individual Data Model Object (DMO). The dealership wants to create separate identity resolution rulesets for B2B and B2C customers so that each audience can be unified independently for segmentation and activation.
Which approach should the Marketing Cloud Next Consultant recommend to ensure B2B and B2C customers are processed by separate identity resolution rulesets?
Answer: B
Explanation:
Data 360 supports source-record filters directly on identity resolution rulesets . These filters act as preprocessing gates that determine which records from the ruleset's primary DMO enter that particular unification pipeline.
This is exactly the appropriate architecture when two populations share the same primary DMO but must be unified independently. The consultant can configure criteria distinguishing B2B from B2C records and apply complementary filters to the respective rulesets.
Salesforce specifically identifies separation of B2B and B2C populations as a valid ruleset-filter use case.
For example, records can be filtered using an account type, customer type, record type, or other partition-level attribute so that business records don't enter the consumer ruleset and vice versa.
Creating separate DMOs simply to achieve identity-processing isolation isn't necessary when the records properly belong to the same canonical Individual model. Filtering source DLOs also places the separation at the wrong architectural layer because identity-resolution filtering is supported directly at the ruleset level.
The uploaded bank keys B. Salesforce's current ruleset documentation explicitly recommends filters for broad partition-level segregation, including B2B versus B2C scenarios.
Study Guide Reference: Data Modeling, Identity Resolution and Segmentation # Identity Resolution # Ruleset Source Filters.
NEW QUESTION # 86
Northern Trail Outfitters wants to automatically send a promotional email to new CRM Contacts using Marketing Cloud Next.
Before the email can be sent, what should the Marketing Cloud Next Consultant configure to ensure a new contact is eligible to receive promotional communications?
Answer: C
Explanation:
Option B is the currently verified Salesforce-supported approach. Marketing Cloud Next treats a prospect, Lead, or Contact without applicable consent as opted out by default. Therefore, before a new Contact can receive promotional marketing, a valid opt-in must exist for the relevant contact point, channel, and communication subscription .
Salesforce's current 2026 guidance states that consent changes originating from CRM record creation or updates should be handled with an Automation Event-Triggered Flow using the Prospect, Lead, Contact, or Related Record Change event, followed by the Consent Request/Create Consent action . That supported action writes the consent so that Marketing Cloud Next honors it at send time.
Option C is explicitly insufficient because HasOptedOutOfEmail isn't automatically synchronized with Marketing Cloud Next consent. Option A is misleading because directly manipulating a separate CRM consent record doesn't itself establish supported Marketing Cloud Next sending consent.
The uploaded document keys this question as A. However, Salesforce's updated July-August 2026 documentation supersedes that approach and explicitly directs implementations to Create Consent/Consent Request.
Study Guide Reference: Consent # Creating and Updating Consent # Create Consent / Consent Request Flow Action.
NEW QUESTION # 87
Northern Trail Outfitters needs to update an individual's consent in near real time when a customer submits a web form.
Which solution should the Marketing Cloud Next Consultant recommend?
Answer: C
Explanation:
An Automation Event-Triggered Flow with the supported consent action is the correct architecture for processing consent immediately after a Marketing Cloud Next form submission. Salesforce specifically states that Create Consent can be used in event-triggered flows to automate consent updates based on events such as a form submission .
Current Salesforce documentation commonly labels the Flow element Create Consent , while some recent support guidance still refers to the underlying operation as Consent Request . The functional purpose is the same in this question: update the contact point's OPT_IN or OPT_OUT state for the appropriate channel and communication subscription through the supported Marketing Cloud Next consent service.
Options B and C are incorrect because directly mapping ingestion or transformation results into the Communication Subscription Consent DMO isn't the supported consent-writing path. Salesforce's August 17,
2026 guidance specifically states that landing-page form consent should be paired with an Automation Event- Triggered Flow and Consent Request action.
The uploaded master correctly keys A.
Study Guide Reference: Consent # Automating Consent # Form Submission, Automation Event- Triggered Flow, and Create Consent/Consent Request.
NEW QUESTION # 88
Northern Trail Outfitters has enabled Email Engagement Scoring in Marketing Cloud Next to generate predictions for email opens, clicks, and unsubscribes.
Which data model object (DMO) stores these predictive engagement scores for reporting and segmentation?
Answer: A
Explanation:
The correct Data Model Object is Email Engagement Score . Einstein Engagement Scoring evaluates historical email engagement behavior and creates predictive attributes that can be used to understand the likelihood of future customer actions. Salesforce identifies the Email Engagement Score DMO as the Data
360 object used for reporting on engagement personas and likelihood measures such as email opens, clicks, and subscription-related behavior.
The DMO can also be incorporated into Data Graph relationships. For example, when an Einstein Decision element relies on Engagement Scoring, Salesforce requires the Contact Point Email DMO to include the Einstein Engagement Score DMO as a related object and the appropriate attributes, such as Email Click Likelihood and Email Open Likelihood.
"Email Engagement Metrics" and "Email Engagement Profile" aren't the standard DMOs Salesforce specifies for these predictive outputs. They therefore don't represent the correct persisted scoring object.
The uploaded master bank also identifies option C as the keyed answer.
Study Guide Reference: Agentforce and AI Innovation # Einstein capabilities in Marketing Cloud Next; Analytics and Performance concepts # Data 360 reporting for Einstein features.
NEW QUESTION # 89
A Marketing Cloud Next Consultant needs to suppress subscribers from an upcoming audience flow who have a low probability of opening or clicking email content.
Which Einstein feature should the consultant use?
Answer: B
Explanation:
Einstein Engagement Scoring is designed to predict the likelihood that an individual subscriber will engage with future email communications. Salesforce states that the model predicts email opens and clicks, subscriber retention, web conversion, and overall engagement for individual consumer contacts.
Those predicted likelihood values can be used to identify customers with low engagement propensity. A marketer can therefore segment, filter, suppress, or branch customers according to their predicted open or click probability before an audience flow sends a message.
Send Time Optimization solves a different problem. STO assumes that a recipient is going to receive the communication and predicts when that person is most likely to engage. It doesn't primarily determine whether a low-propensity customer should be excluded. Messaging Insights isn't the predictive subscriber-level engagement scoring capability described in this requirement.
Salesforce also stores the output in the Email Engagement Score DMO , which supports reporting and audience analysis based on open, click, and subscription likelihood.
The uploaded master bank correctly keys A. Salesforce's current Einstein Engagement Scoring model card confirms that it predicts opens, clicks, retention, and overall engagement.
Study Guide Reference: Agentforce and AI Innovation # Einstein Engagement Scoring # Predictive Audience Optimization.
NEW QUESTION # 90
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