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| Section | Weight | Objectives |
|---|---|---|
| Analytics & Performance Insights | 8% | - Marketing data visualization and performance insights - Standard dashboards and reporting selection |
| 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 |
| Consent | 13% | - Consent banner configuration and collection methods - Consent management concepts and regulatory compliance - Consent data objects and records management |
| Agentforce & AI Innovation | 11% | - Predictive AI capabilities and use cases - Marketing Agents for campaign and audience automation - Conversational messaging with Agentforce |
| Campaign Design, Flow Orchestration & Content | 30% | - Activation templates, landing pages and multi-channel design - Flow design, orchestration, triggers and messaging - Personalization with Handlebars and dynamic content |
| Platform Setup & Governance | 13% | - Marketing Cloud Next environment provisioning and Data 360 setup - Domain authentication (DKIM, SPF) and IP configuration - Business Units, governance model, roles and permissions |
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90. Frage
A marketing manager wants to launch a new campaign targeting customers who have recently browsed winter boots but have not purchased. The manager is not an expert in data structures or SQL. They need a way to translate this business requirement into a functional audience segment without technical assistance.
Which Marketing Cloud Next capability should the manager use for this task?
Antwort: B
Begründung:
AI-Assisted Segment Creation , currently documented by Salesforce as Einstein Segment Creation, is designed precisely for marketers who understand the desired business audience but don't want to manually navigate complex Data 360 schemas or construct segmentation logic.
Salesforce allows the marketer to describe the target audience using natural language. Einstein interprets the description, identifies relevant attributes from the organization's Data 360 model, and generates a proposed segment definition. The marketer can then review suggested attributes, refine the request conversationally, recount the audience population, and create the segment.
For example, the manager could describe customers who recently browsed winter boots but have not purchased them. Einstein can translate that marketing intent into applicable behavioral and purchase criteria.
Quick Filters don't provide the same natural-language business-to-segment translation. Manually creating AND/OR rules would work technically, but it contradicts the requirement that the manager shouldn't need technical understanding of the underlying attributes.
The source bank identifies B.
Study Guide Reference: Agentforce and AI Innovation # Einstein Segment Creation # Natural-Language Audience Building.
91. Frage
Northern Trail Outfitters (NTO) is designing a monthly 'Trail Rewards' email in Marketing Cloud Next. NTO needs to dynamically personalize the template using two data points:
* The number of 'Days Since Last Login' to nudge digital adoption
* The customer's current 'Loyalty Tier' based on their lifetime spend to apply the correct branding To minimize Flex Credit consumption within their Digital Wallet, how should a Marketing Cloud Next Consultant configure the template?
Antwort: A
Begründung:
Option A applies the least computationally intensive feature appropriate to each requirement. Days Since Last Login is fundamentally a row-level derived value based on an existing login-date attribute, so a Data
360 data-stream formula can derive a supplemental field without requiring an analytical aggregation pipeline.
Salesforce supports formula fields derived from other fields, functions, and operators during data-stream processing.
Loyalty Tier based on lifetime spend , however, requires aggregation across potentially many transaction records for each customer. A batch calculated insight is designed for that type of multi-record calculation.
Because the email is monthly, a daily batch calculation is adequate; there is no business need to pay the higher processing cost associated with streaming computation.
Streaming calculated insights consume Data 360 Streaming Pipeline Flex Credits based on processed records. Batch calculated insights are billed under Data 360 Prep according to underlying records processed.
Therefore, unnecessary streaming should be avoided when sub-second freshness isn't required.
A formula-field caveat is that Salesforce recalculates a data-stream formula when the corresponding record is refreshed or changed, so implementation scheduling must ensure the value is sufficiently current for the monthly send.
The master bank keys A.
Study Guide Reference: Data Modeling, Identity Resolution and Segmentation # Data 360 Calculations # Formula Fields, Calculated Insights, and Credit Optimization.
92. Frage
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?
Antwort: C
Begründung:
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.
93. Frage
A company is evaluating which Salesforce features require Marketing Cloud Next Advanced Edition versus the Growth Edition. The implementation team has a list of features they plan to use.
Which feature from their list mandates the use of Advanced Edition?
Antwort: C
Begründung:
Among the available choices, A is the intended Advanced-edition capability. Standard content creation is a core Marketing Cloud Next capability available in both Growth and Advanced editions, so C doesn't require Advanced. Standard Salesforce reporting likewise isn't inherently restricted to Marketing Cloud Next Advanced, eliminating B.
The edition distinction becomes important for account-oriented marketing capabilities. Salesforce's current implementation documentation identifies Account scoring as an Advanced Edition capability and requires identity resolution for both Individual and Account objects before configuring people-and-account scoring.
This is why account-level identity infrastructure appears in Advanced-focused implementation scenarios.
There is an important technical nuance: Salesforce's general Identity Resolution documentation describes creating custom rulesets for either Individual or Account and doesn't independently state, in the current public article, that merely creating an Account ruleset is universally Advanced-only. The Advanced restriction is explicitly documented around the account-oriented scoring capability that depends on that ruleset. Within the supplied options and certification context, however, A is the valid answer ; B and C are clearly Growth- supported functionality.
The master bank also keys A.
Study Guide Reference: Platform Setup and Governance # Editions and Feature Availability # Growth vs. Advanced.
94. Frage
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?
Antwort: C
Begründung:
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.
95. Frage
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