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Marketing Cloud Next Consultant

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Marketing Cloud Next Consultant Study Checklist
  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Marketing Cloud Account Engagement Specialist
  • Marketing Cloud Next Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Communications AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • Public Sector Solutions AP
  • Revenue Cloud Consultant
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Next Consultant
  • Tableau Architect
  • Slack Administrator
  • Slack Consultant
  • Slack Developer

Checklist progressProgress

0/234Learned

  • Core org edition requirements for Marketing Cloud Next
  • Setup responsibilities of Salesforce admin, Data 360 architect and marketing admin
  • Setup assistant and guided setup paths
  • Data 360 provisioning and the Salesforce CRM connector
  • Marketing Cloud Next enablement prerequisites and troubleshooting
  • Marketing data space selection during enablement
  • Required marketing data kits and channel add-on data kits
  • Deploying and updating marketing data kits
  • When to deploy the optional Sales data kit
  • Marketing Cloud Admin versus Marketing Cloud Manager permission sets
  • Granular marketing permissions for custom permission sets
  • When to create business units versus a single marketing setup
  • Business unit and data space mapping for data isolation
  • Enabling business units and what the initial business unit inherits
  • Business-unit-level versus org-level setup tasks
  • Business unit membership eligibility and permission set assignment
  • Marketer-Standard versus Marketer-ReadOnly business unit roles
  • CMS workspace contributor roles for marketing content
  • Organizing marketing workspaces within a business unit
  • Relating a workspace to a business unit for personalization
  • Adjusting a business unit member's CMS workspace access
  • Sharing content between marketing workspaces
  • Common assets versus workspace sharing across business units
  • Campaign scoping to a business unit's data and content
  • Communication subscription and channel scope across business units
  • Business unit change, deactivation and capacity constraints
  • Default and custom domains across email assets, SMS and landing pages
  • Authenticated domains versus authorized email domains
  • SPF, DKIM and DMARC responsibilities for Marketing Cloud Next email
  • Sending subdomain versus root domain for authenticated email
  • Authenticated sending domain setup and verification in Unified Messaging
  • Functional subdomains and inbound mail routing for replies and bounces
  • Troubleshooting a sending domain that fails verification
  • Automatic versus self-managed dedicated IP provisioning
  • Role of consent management in compliance and customer trust
  • Explicit opt-in principle and default opted-out status
  • Subscription-based consent model versus a single opt-in field
  • Centralized consent in Data 360 across clouds
  • Consent checks for promotional versus transactional sends
  • Unsubscribe from all versus individual subscription opt-outs
  • SMS and RCS opt-in requirements
  • SMS keyword opt-outs and sender-code consent scope
  • WhatsApp consent and opt-out rules
  • Communication subscription, channel type and timing objects
  • Contact points versus the Unified Individual as the consent anchor
  • Communication Subscription Consent as the opt-in status record
  • Consent composite key across subscription, channel and contact point
  • Choosing a supported method to create or update consent records
  • Consent Status component for record-level consent updates
  • Flow types and actions for automated consent writes
  • Create Consent action input values
  • Capturing consent from landing page and signup form submissions
  • Double opt-in consent flow design
  • Preference pages for subscriber-managed consent
  • Initial consent import for existing records at go-live
  • Consent import file structure and processing rules
  • Consent tab import versus Leads or Contacts tab import
  • Consent updates after a contact point changes
  • Retiring a communication subscription versus deleting it
  • Reviewing consent records, audit history and list health
  • Unsupported consent write paths and their consequences
  • Custom consent fields versus the Communication Subscription Consent model
  • Consent to track versus communication subscription consent
  • Permissions for creating and configuring consent banners
  • Consent banner setup for marketing landing pages
  • Consent banner on external websites with a website connector and embed code
  • Visitor banner choices and their effect on activity tracking

Describe data object concepts in Data 360 and how they are used to connect, harmonize, unify, and activate customer data for audience segmentation and content personalization

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Data lake objects versus data model objects

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Data ingestion versus Zero Copy data federation

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Profile, engagement and other DMO categories

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Individual and contact point data model objects

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Standard versus custom DMOs when harmonizing source data

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Marketing Cloud Next data kits and the data streams they deploy

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Marketing objects versus Data 360 data model objects

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Relating DMOs to the Unified Individual for segment filters

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Choosing the Segment On object for a marketing segment

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Direct versus related attributes in segmentation

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Calculated insights in segmentation and personalization

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Activation, activation targets and activation membership

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Data graph field exposure for personalization and decisioning

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Non-profile data sources for personalized marketing content

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Segment, data graph and Salesforce core data in campaign components

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Validating data hydration across DMOs

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Given a business requirement, identify how CRM data, including objects, records, and Actionable Lists, can be ingested and used for audience segmentation, activation, and content personalization

0/7

  • Prospect, lead and contact records for marketing audiences
  • Ingesting Salesforce CRM objects into Data 360
  • How CRM people records become unified individuals
  • Reading CRM data directly versus through Data 360
  • Segments versus actionable lists as campaign audiences
  • Segment requirements for Marketing Cloud Next campaign audiences
  • Creating and maintaining actionable lists

Given a business requirement, configure Identity Resolution to link multiple data sources into a unified profile

0/12

  • Unified profiles versus golden records
  • Data mapping prerequisites for identity resolution
  • Generated versus custom identity resolution rulesets in Marketing Cloud Next
  • Primary data model object choice for a custom ruleset
  • Identity resolution rulesets for business unit data spaces
  • Exact, exact normalized and fuzzy match methods
  • Match rule and criteria design for accuracy versus consolidation
  • Matching on party identifiers and external identity links
  • Lead-to-contact and device-to-known identity match rules
  • Reconciliation rules for unified profile field values
  • Ruleset processing frequency and scheduled versus real-time matching
  • Validating ruleset results with consolidation rates

Demonstrate knowledge of Data 360 consumption-based entitlements and evaluate how marketing automation design decisions impact platform consumption and usage

0/9

  • Data 360 credits, consumption cards and entitlements
  • Data 360 Profiles add-on license and metered versus limited usage
  • Monitoring and attributing Data 360 consumption in Digital Wallet
  • CRM connector ingestion versus external and streaming data streams
  • Data stream refresh mode, frequency and scope for consumption
  • Zero Copy federation and data shares versus ingestion
  • Identity resolution ruleset design and credit consumption
  • Segment publishing design and Data 360 consumption
  • Standard versus real-time data graphs for personalization
  • Personalization setup prerequisites and the default data graph
  • Merge fields for inserting customer attributes into content
  • Saved expressions for cross-object merge fields
  • Dynamic content variations with targeting rules
  • Variation priority and the default variation
  • Linking versus cloning personalization points
  • Repeater components for displaying a series of items
  • Repeaters powered by Salesforce Personalization recommenders
  • Handlebars in code view for conditional logic and data lookups
  • Choosing among AMPscript, Handlebars and no-code personalization methods
  • Campaign and marketing flow relationship rules
  • Starting a campaign flow from a trigger, template or Quick Start
  • Choosing an audience flow data source
  • Segment flow scheduling and pre-run segment republish
  • Broadcast flows and dynamic segment eligibility
  • Automation event-triggered flows for behavior-driven messaging
  • Retail marketing triggers for shopper behavior events
  • On-demand flows for high-priority transactional sends
  • Activation-triggered flows for downstream activation targets
  • Flow re-entry settings for repeat participation
  • Flow type constraints on available message and path elements
  • Default profile data graph as the personalization data source
  • Unified Individual DMO data source versus a profile data graph
  • Lookup data graph for non-profile reference data
  • Event data source for triggered message personalization
  • Salesforce Record data source for current CRM object data
  • Content variables populated by Flow at send time
  • Apex class data provider for custom personalization data
  • Offer data source for tailored promotions
  • Personalization Recommender data source requirements
  • Activation data source in marketing email content
  • Data source availability by content type and channel
  • Consequences of changing or mismatching content data sources
  • Decision element outcome evaluation and default path
  • Decision conditions on data graph and record attributes
  • Time-based waits versus Wait Until Event
  • Choosing among SMS, RCS and WhatsApp send elements
  • Mobile app push, in-app and flash message elements
  • Message element settings for consent, tracking and send time
  • Path Experiment versus Personalize Paths
  • Automated versus manual path selection in a Path Experiment
  • Einstein Decision element for engagement-based routing
  • Exit rules versus the Exit from a Flow element
  • Routing individuals to on-demand flows with Send to a Flow
  • Create Consent element for in-flow consent updates
  • Determine CRM Record for Individual element
  • Marketing completion actions for follow-up tasks
  • Flow elements that hand off to Marketing Cloud Engagement
  • Permissions for building marketing flows and decision branching
  • Activation templates for reusable contact point configuration
  • Activation template data space, DMO and platform scope
  • Contact point DMO mappings required for activation
  • Source priority order for selecting a contact point value
  • Primary, personal and business contact point preferences
  • Choosing among multiple contact point values from one source
  • Source priority versus reconciliation rules for contact point values
  • Contact point filters using contact point and consent DMOs
  • Contact point filters versus activation membership filters
  • Unified Individual versus Individual as activation membership
  • Choosing a landing page creation method and its hosting site
  • Signup Form campaign versus separately built landing page, form and flow
  • Form structure and required field unique names
  • Form write data sources and code-view input mapping
  • Form submission processing in the landing page's flow
  • Landing page personalization data sources
  • Landing page publishing and URL alias behavior
  • Landing page SEO properties
  • Permissions and CMS roles for landing page work
  • Marketing Landing Pages site configuration considerations
  • Choosing a marketing agent for a business use case
  • Prerequisites for enabling marketing agents
  • Campaign Creation agent workflow from brief to saved campaign
  • Grounding a campaign brief for agent-generated campaigns
  • Saving an agent-drafted campaign and unlinking its brief
  • Campaign summaries and insights through the Campaign Creation agent
  • Creating audience segments with Einstein generative AI
  • Agentic Content workspace grounding and generation
  • Agentic Content variants, translations and audience changes
  • Agentic Content publishing to CMS and create versus publish permissions
  • AI content refinement in Content Builder versus Agentic Content
  • Customizing the flows behind Campaign Creation agent actions
  • Extending the Campaign Creation agent with prompt template custom actions
  • Channel, flow, agent and Data 360 roles in conversational marketing
  • Choosing SMS, WhatsApp, email or RCS for a conversational use case
  • Flow design to send, listen for and route conversational replies
  • Conversational option in Agentforce-drafted campaigns
  • Conversational Email implementation tasks and setup order
  • Enabling Conversational Email on From Addresses and reply filtering
  • Handling incoming WhatsApp messages that match no keyword or conversation
  • Agentforce Service agent versus Employee agent in conversational marketing
  • Subagents versus actions for conversational agent behavior
  • Human rep escalation and handoff for conversational replies
  • RCS conversational support routing types and fallback queue
  • Unified Messaging for shared marketing and service conversations
  • Consent and AI disclosure controls in conversational messaging
  • Predictive Einstein features versus generative Agentforce features
  • Prerequisites for enabling Einstein predictive features
  • When to use Einstein Send Time Optimization
  • When to use Einstein Engagement Frequency
  • When to use Einstein Engagement Scoring
  • Einstein predictive scores in flows through data graphs
  • When to use Einstein Metrics Guard
  • Analytics tab engagement dashboards and report groups by channel and asset
  • Person-level engagement components versus aggregate engagement dashboards
  • Content Performance dashboard for cross-channel content and variation results
  • Deliverability dashboard for email failure and bounce analysis
  • Campaign Performance dashboard versus the Marketing Performance Intelligence dashboard
  • Marketing Intelligence attribution and cross-channel dashboards
  • B2B Analytics for Marketers dashboards for pipeline and revenue questions
  • Marketing Performance Intelligence app versus preconfigured Analytics tab reporting
  • Marketing Performance Intelligence versus Marketing Intelligence
  • Marketing engagement data on CRM record pages for sales teams
  • Campaign record Overview and Performance sidebar insights
  • Related Landing Pages for campaign landing page metrics
  • Flow Performance analytics on the flow canvas
  • Customizing Analytics tab dashboards and the Analytics page
  • Opportunity Influence for campaign revenue attribution
  • Marketing Performance Intelligence installation prerequisites
  • Marketing Performance Intelligence install versus view permission sets
  • Updating the Marketing Performance Intelligence app for new dashboards
  • Choosing an analytics tool for Data 360 marketing data
  • Reporting on Einstein engagement predictions in Data 360
  • Investigating unsent emails through Email Engagement DMO data

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  1. Marketing Cloud Next Consultant Checklist
  3. Section 3: Data Modeling, Identity Resolution & Segmentation

Data graph field exposure for personalization and decisioning

Explainer

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Practice Question

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This is one of 234 concepts on the Salesforce Marketing Cloud Next Consultant exam.

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Next conceptNon-profile data sources for personalized marketing content

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  1. Marketing Cloud Next Consultant Checklist
  3. Section 3: Data Modeling, Identity Resolution & Segmentation
Data graph field exposure for personalization and decisioning

Explainer

Learn More

Practice Question

Studying for this exam?

This is one of 234 concepts on the Salesforce Marketing Cloud Next Consultant exam.

Looking for practice exams? Click below to try one!

Keep going

Next conceptNon-profile data sources for personalized marketing content

Explainer

A data graph is a Data 360 data object built from a primary data model object (DMO) and its related objects. In Marketing Cloud Next, it works like a map that tells the app which DMO objects and fields it may use for personalization and decisioning. Data that exists in Data 360 is not automatically available in Marketing Cloud Next, so a field must be added to the data graph before emails, landing pages, or flow decisions can use it.
Core information
  • Data moves through layers in Data 360. Raw source data lands in a data lake object (DLO) as it came in. It is then mapped to standardized DMOs, and identity resolution stitches those records into one Unified Individual profile. Segments are built on this DMO layer, but personalization and decisioning read from the data graph, which sits on top of it.
More details and nuances
  • If a field exists in a DMO but is not in the data graph, Marketing Cloud Next cannot use it in merge fields, dynamic content, or decision splits. The fix is to confirm the field exists in a DMO and then add that object and field to the data graph. A missing segment is a different problem: the data must come in as a data stream, be mapped to a DMO such as Sales Order, and that DMO must be related to Unified Individual.

Explainer

A data graph is a Data 360 data object built from a primary data model object (DMO) and its related objects. In Marketing Cloud Next, it works like a map that tells the app which DMO objects and fields it may use for personalization and decisioning. Data that exists in Data 360 is not automatically available in Marketing Cloud Next, so a field must be added to the data graph before emails, landing pages, or flow decisions can use it.
Core information
  • Data moves through layers in Data 360. Raw source data lands in a data lake object (DLO) as it came in. It is then mapped to standardized DMOs, and identity resolution stitches those records into one Unified Individual profile. Segments are built on this DMO layer, but personalization and decisioning read from the data graph, which sits on top of it.
More details and nuances
  • If a field exists in a DMO but is not in the data graph, Marketing Cloud Next cannot use it in merge fields, dynamic content, or decision splits. The fix is to confirm the field exists in a DMO and then add that object and field to the data graph. A missing segment is a different problem: the data must come in as a data stream, be mapped to a DMO such as Sales Order, and that DMO must be related to Unified Individual.