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Tableau Next Consultant

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Tableau 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

0/189Learned

  • Role and benefits of the Tableau Semantics layer
  • Where semantic models are consumed across Salesforce and Tableau
  • Methods for creating a semantic model
  • Selecting data objects and fields for a semantic model
  • Relationships between data objects in a semantic model
  • Connected model structure versus island objects
  • Logical views for combining objects with joins and unions
  • Semantic calculated fields and reusable semantic definitions
  • Metrics built on semantic models
  • Navigating and testing a model in the Semantic Model Builder
  • Access layers for semantic models
  • Combining insights from multiple semantic models
  • Object, field and model descriptions for agent context
  • Semantic model object requirements for agentic analytics
  • Data 360 prerequisites and data flow for Tableau Next
  • Data 360 permission sets for data setup tasks
  • Data streams as the ingestion pipeline into Data 360
  • Data 360 connector options for common data sources
  • Data stream categories and their downstream effect
  • Primary keys and composite keys for data streams
  • Data lake objects versus data model objects
  • Mapping DLOs to DMOs in the Customer 360 Data Model
  • Starter data bundles and Simple Start for Salesforce CRM data
  • Changing a data stream schema after creation
  • Batch versus streaming data transforms
  • Data federation versus data ingestion into Data 360
  • Batch, streaming and real-time ingestion selection
  • Choosing a data source for Tableau Next workspace analysis
  • Data 360 objects versus a Tableau published data source for a semantic model
  • Choosing the layer for derived fields and business logic
  • Data spaces for separating data by brand, region or business unit

Understand how generative AI capabilities support the creation of Tableau Next components

0/8

  • Einstein generative AI prerequisites for Tableau Next AI features
  • Tableau Next settings that turn on AI-assisted authoring tools
  • Draft with Einstein for natural language data preparation
  • Semantic modeling agent for generating and refining semantic models
  • AI-suggested relationships between data model objects
  • AI Semantic Description Generator for semantic model entities
  • Calculated fields from natural language prompts
  • Generative AI summaries and automated visuals in Tableau Next authoring

Explain how to assess and ensure Agentic readiness

0/12

Phases of a Tableau Agent implementation for conversational analytics

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Analytics Agent Readiness property and its role for Tableau Agent

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Enabling Analytics Agent Readiness in the Semantic Model Builder

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Defining agent use cases and collecting test utterances

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Object and field naming for agent comprehension

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Descriptions that reduce ambiguity for Tableau Agent

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Primary Name Field and its role in agent answers

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Managing duplicate and similar calculated fields for AI readiness

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Promoting calculated fields to metrics as semantic anchors

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Business preferences for business-specific terminology and logic

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Semantic model testing versus Semantic Model AI Optimization checks

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Q&A Calibration and verified questions for agent accuracy

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Understand how the Analytics Agent enables users to explore data and generate insights

0/15

  • Conversational analytics versus other Tableau Agent capabilities
  • How Tableau Agent queries the semantic model
  • Surfaces that support Tableau Agent conversational analytics
  • Opening the Agentforce panel and selecting the analytics agent
  • Components of a conversational analytics response
  • Supported analytical question types for conversational analytics
  • Dimension value and date filters in agent questions
  • Current-user ('My') questions in conversational analytics
  • Metadata discovery questions about available data and definitions
  • Multi-turn conversation memory and follow-up questions
  • Changing the visualization type in a conversation
  • Clarifying questions and guided alternatives from the agent
  • Dashboard and metric context available to the agent
  • Metric insights with and without Tableau Agent enabled
  • Agent scoping with fallback semantic models

Understand how the Analytics Agent delivers proactive data alerts and notifications

0/8

  • Proactive insights and data alerts in Tableau Agent
  • Data alert trigger conditions and evaluation frequency
  • User workflow and permissions for creating a metric data alert
  • Managing and acting on received data alert notifications
  • Admin configuration of data alert delivery channels
  • Receiving proactive data alerts in Slack
  • Proactive alerts setting and its conversational analytics prerequisite
  • Agent subagents and actions required for data alerts
  • Roles of Data 360, Tableau Semantics, and Tableau Next in the analytics stack
  • Tableau Next versus CRM Analytics and Tableau by Tableau products
  • Semantic models from Tableau Cloud published data sources and Salesforce trust
  • Embedding Tableau Next assets on Lightning pages with Lightning App Builder
  • Tableau Next entry points in Slack
  • Copy component code quick start for embedding in web apps
  • External Client Apps for Tableau Next embedding authentication
  • Choosing where to surface Tableau Next insights for a user workflow
  • Tableau Next Apps for Salesforce and the business areas they serve
  • C360 Semantic Model for cross-cloud KPIs
  • Licenses and permission sets for Tableau Next included apps
  • Tableau Next Marketplace template catalog and installation
  • AgentExchange app packages in Tableau Next Marketplace
  • Semantic model templates versus building a model from scratch
  • Dashboard templates versus app templates built from a workspace
  • App Hub versus Templates page for managing templated apps
  • Tableau Next REST API for programmatic asset and access management
  • Tableau Semantics APIs for semantic model authoring and querying
  • Tableau Next Embedding SDK for custom web apps
  • Embedding SDK prerequisites and causes of embedded content failures
  • Tableau Next MCP server for external AI agents
  • App Template Framework develop, install, and manage tools
  • App templates versus standard packaging for deploying Tableau Next solutions
  • Tableau Next developer tools versus Tableau Cloud and Server APIs
  • Prerequisites and setup order for activating Tableau Next
  • Enabling Tableau Next in Tableau Next Setup
  • Guided Tableau Next Setup stages
  • Semantics AI and semantic model AI-readiness settings
  • Metric Insight Summaries activation and behavior
  • Tableau Next Mobile setup
  • Slack for Tableau Next versus Tableau Agent in Slack and Slackbot MCP
  • Tableau Next licenses versus permission sets
  • Admin and analyst permission set tiers in Tableau Next
  • Consumer permission set tiers and Limited Consumer access
  • Provisioning existing users through Tableau Next User Management
  • Granting the Tableau Next Admin permission set
  • Unmetered Tableau Next licenses and custom permission sets
  • Multi-layered access control in Tableau Next
  • Analytics groups for managing access to Tableau Next assets
  • Object-level versus record-level Data 360 policies
  • Row-level security with allow policy patterns and entitlement tables
  • Default record visibility and policies in guided Tableau Next setup
  • Platform prerequisites for activating Tableau Agent
  • Tableau Next and Data 360 permission sets for Tableau Agent users
  • Metadata Knowledge Graph and Semantic Enrichments setting in Data 360
  • Tableau Agent, Data Analysis and Q&A Calibration settings
  • Creating a Tableau Agent from the Analytics and Visualization template
  • Analytics and Visualization template subagents and the capabilities they enable
  • Granting users access to an activated Tableau Agent
  • Guided setup versus manual setup for Tableau Next AI features
  • Agent Scoping for Tableau Agent without asset context
  • Dashboard design best practices for purpose, audience and layout
  • Building visualizations on semantic model data in the Visualization Builder
  • Choosing a chart type or data view for the analytical question
  • Marks properties for color, labels and size encoding
  • Calculated fields and dimension hierarchies in visualizations
  • Analytics pane forecasting and reference lines
  • Visualization-level versus element-specific Style pane formatting
  • Conditional formatting versus table color encoding for highlighting
  • Templated versus custom Tableau Next dashboards
  • Adding existing workspace assets to a dashboard
  • Choosing dashboard widget types for content and context
  • Dashboard grid presets, layout properties and Reflow
  • Dashboard widget colors versus Visualization Builder settings
  • Multi-page dashboards and the Navigation widget
  • Visualization filters, filter widgets and mark-selection filtering
  • Parameter widgets for what-if scenario exploration
  • Controlling which widgets receive dashboard filters and parameters
  • Initial values and dynamic defaults on dashboard load
  • Multi-model filtering across semantic models
  • Visualization actions versus dashboard widget actions
  • Salesforce record actions on visualization fields
  • Dynamic URL navigation actions on visualization fields
  • Taking action from a visualization record field
  • Navigate To actions from dashboard widgets
  • Parameter actions for interactive dashboards
  • Flow eligibility and limits for dashboard Flow actions
  • Flow action input mapping from dashboard context
  • Troubleshooting actions users cannot see or run
  • Personal Org versus the Tableau Next production org
  • Personal Org license and permission set requirements
  • Enabling Personal Orgs for an organization
  • Creating a Personal Org for an analyst
  • Reusing production assets in a Personal Org
  • Sharing Personal Org workspaces and collaborator capabilities
  • Data governance for assets in a Personal Org
  • License removal versus Personal Org deletion during offboarding
  • Workspace strategy for organizing and governing analytic assets
  • Asset access roles and default asset privacy
  • Workspace sharing and role inheritance by member assets
  • Asset references versus owned assets across workspaces
  • General access settings and access precedence
  • Analytics groups for team-based asset sharing
  • Copying links to visualizations and dashboards
  • Permission sets that gate sharing and editing of shared assets
  • Semantic model access prerequisites
  • Metric access through semantic model sharing
  • Object-level permissions as a semantic model access path
  • Asset sharing versus Data 360 governance of underlying data
  • Choosing change sets, data kits, or Salesforce CLI for Tableau Next deployment
  • Tableau Next in a Data 360 sandbox
  • Permissions for change set and data kit deployments
  • Semantic model deployment requirement for Tableau Next assets
  • Building a DevOps data kit that contains Tableau Next assets
  • Data kit publishing sequence for Tableau Next dependencies
  • Moving a data kit from sandbox to the target org
  • Release version compatibility for data kit deployments
  • Change set deployment of Tableau Next assets
  • Deploying Tableau Next assets to Data 360 companion orgs
  • Submitting Personal Org assets for promotion
  • Reviewing promotion requests in Task Manager
  • Guided flow versus Salesforce CLI promotion flow
  • Guided flow promotion steps and completion outcomes
  • Salesforce CLI promotion prerequisites and sequence
  • Promotion warnings, blocks, and failure recovery

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  1. Tableau Next Consultant Checklist
  3. Agentic Experiences

Phases of a Tableau Agent implementation for conversational analytics

Explainer

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Practice Question

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This is one of 189 concepts on the Salesforce Tableau Next Consultant exam.

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Next conceptAnalytics Agent Readiness property and its role for Tableau Agent

Checklist progress

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0 of 189 concepts learned

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  1. Tableau Next Consultant Checklist
  3. Agentic Experiences
Phases of a Tableau Agent implementation for conversational analytics

Explainer

Learn More

Practice Question

Studying for this exam?

This is one of 189 concepts on the Salesforce Tableau Next Consultant exam.

Looking for practice exams? Click below to try one!

Keep going

Next conceptAnalytics Agent Readiness property and its role for Tableau Agent

Explainer

Tableau Agent brings conversational analytics to Tableau Next, so people can ask questions about their data in plain language and get answers from an agent. Setting it up well follows five phases: define what the agent should help with, set up the agent, make the semantic model AI-ready, optimize and test the model, and roll the agent out in stages. Think of the agent as a junior analyst you mentor: the use cases, data, and business context you give it are what make its answers accurate.
Core information
  • Conversational analytics in Tableau Agent runs on the Data Analysis subagent, which is part of the Analytics and Visualization agent template. The five phases are: (1) define how Tableau Agent can help, (2) set up Tableau Agent in Tableau Next, (3) make the semantic model AI-ready, (4) optimize, test, and calibrate the semantic model, and (5) progressively roll out the agent.
More details and nuances
  • Implementation is iterative, not linear. Testing often sends you back to earlier work, such as revisiting business preferences during optimization, and you keep testing and updating until responses match what you expect.

Explainer

Tableau Agent brings conversational analytics to Tableau Next, so people can ask questions about their data in plain language and get answers from an agent. Setting it up well follows five phases: define what the agent should help with, set up the agent, make the semantic model AI-ready, optimize and test the model, and roll the agent out in stages. Think of the agent as a junior analyst you mentor: the use cases, data, and business context you give it are what make its answers accurate.
Core information
  • Conversational analytics in Tableau Agent runs on the Data Analysis subagent, which is part of the Analytics and Visualization agent template. The five phases are: (1) define how Tableau Agent can help, (2) set up Tableau Agent in Tableau Next, (3) make the semantic model AI-ready, (4) optimize, test, and calibrate the semantic model, and (5) progressively roll out the agent.
More details and nuances
  • Implementation is iterative, not linear. Testing often sends you back to earlier work, such as revisiting business preferences during optimization, and you keep testing and updating until responses match what you expect.