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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 2: Data Setup | 20% | - Data 360
|
| Topic 3: Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Topic 4: Basic Setup & Admin | 10% | - User Access
|
| Topic 5: Agentic Experiences | 25% | - Generative AI
|
| Topic 6: Managing Workspaces & Orgs | 10% | - Personal Org
|
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NEW QUESTION # 62
A Tableau Next Consultant at Ursa Major Solar is creating a Tableau Next dashboard to monitor at- risk opportunities. The sales manager needs a way for sales reps to initiate follow- up tasks for specific opportunities directly from a dashboard widget. This action must trigger an existing Salesforce flow designed for task creation. Which action type should the consultant add to the widget to meet this requirement?
Answer: C
Explanation:
The consultant should configure a Salesforce Flow action on the dashboard widget. Tableau Next supports single-click dashboard actions that can directly initiate Salesforce Flows using dashboard context. Salesforce even documents a virtually identical use case: enabling sales representatives to create a follow-up task directly from a dashboard widget displaying at-risk opportunities.
When configuring the action, the consultant selects Salesforce Flow as the action type, chooses an eligible flow, and maps the flow's input variables to static or dynamic dashboard values. Tableau Next currently supports standard screen flows and autolaunched flows for this capability.
A parameter action is fundamentally an analytical interaction mechanism and is not the native mechanism for executing a Salesforce Flow. Option C refers generally to Salesforce actions on visualization records, but the scenario explicitly identifies a dashboard widget that must initiate an existing Salesforce Flow. The dedicated Flow action is therefore the precise implementation.
This capability illustrates Tableau Next's actionable-analytics design: the user moves directly from recognizing an analytical condition to initiating an operational Salesforce process without leaving the dashboard.
References/Topics: Visualizations and Dashboards - > Single-Click Actions - > Salesforce Flow Actions - > Dynamic Input Mapping.
NEW QUESTION # 63
A Tableau Next Consultant has created a new semantic model and wants to identify potential metadata gaps that might confuse the Analytics Agent. Which tool provides an automated "Readiness Score" and actionable suggestions to improve the model's clarity for the AI?
Answer: B
Explanation:
Optimize Model is the feature designed to assess semantic models for AI readiness and recommend improvements. Salesforce describes Semantic Model AI Optimization as evaluating model quality, diagnosing problems, and supplying guided remediation that improves reliability for Tableau Agent. After the feature is enabled, an Optimize Model indicator appears in Semantic Model Builder and shows the model's AI-readiness strength.
Opening the Optimize Model panel provides a breakdown of AI-readiness indicators together with recommended corrective actions. The rating is recalculated as the semantic model changes, allowing consultants to iteratively improve metadata, structure, and agent compatibility.
Q & A Calibration serves a different purpose: it tests representative natural-language questions and helps calibrate how Tableau Agent responds. Business Preferences provide explicit business context and rules, such as how ambiguous terminology should be interpreted. Neither performs the automated holistic readiness evaluation described in the question.
References/Topics: Data Setup - > Semantic Model AI Optimization - > Optimize Model - > AI-Readiness Strength - > Recommended Actions.
NEW QUESTION # 64
During a Tableau Next implementation, a Tableau Next Consultant is explaining to the admin team when Agent Scoping for Tableau Agent actually takes effect. When is Agent Scoping used?
Answer: A
Explanation:
Agent Scoping is specifically designed for situations in which Tableau Agent receives a question without clear analytical context. Salesforce defines Agent Scoping as the administrator-controlled list of semantic models that Tableau Agent can search when context cannot otherwise determine which model should answer the user's question.
Typical examples include Tableau Agent being used in Slack or embedded on a page that does not contain a Tableau Next dashboard, metric, or other asset capable of supplying context. The configured semantic-model list serves as a fallback list. Tableau Agent can then determine which approved model should be used to resolve the user's request.
Agent Scoping does not run indiscriminately for every question, which eliminates B. When Tableau Agent is invoked from an analytical surface that already provides clear semantic context, that existing context guides the interaction. C is also overly restrictive; Agent Scoping is not limited to metric-details pages or scenarios involving multiple models attached to a metric.
Administrators can configure approved fallback semantic models for active Analytics and Visualization agents using the Data Analysis subagent.
References/Topics: Agentic Experiences - > Tableau Agent - > Agent Scoping - > Fallback Semantic Models
- > Context Resolution.
NEW QUESTION # 65
A Tableau Next Consultant receives a request from a business user to help troubleshoot an issue with setting data alerts using Tableau Agent. The consultant verifies that the business user has been granted a Tableau Next Consumer license and is accessing Tableau Agent from a page with Tableau Next on it. Which reason explains why the business user is unable to set the alert?
Answer: C
Explanation:
The limiting factor in this scenario is the user's license level. Setting proactive data alerts through Tableau Agent is an authoring-oriented capability that requires Tableau Next Creator access. A Consumer user can view and interact with published analytical content, but the Consumer persona does not receive the full set of creation and configuration capabilities needed to define alert conditions.
The fallback semantic model in option B is used when Tableau Agent lacks clear analytical context, such as when a user invokes the agent from a surface that does not already identify the relevant Tableau Next asset.
Here, the user is explicitly accessing Tableau Agent from a page that contains Tableau Next content, so lack of fallback scoping is not the primary issue. Option A can prevent analysis of a particular metric, but the question establishes a broader inability to set the alert and emphasizes that the user has a Consumer license.
The appropriate remediation is therefore to use the Creator-level entitlement for users who must configure proactive alerts. This preserves the distinction between consumption permissions and analytical creation
/configuration permissions.
References/Topics: Agentic Experiences - > Tableau Agent - > Proactive Data Alerts - > Creator Permissions.
NEW QUESTION # 66
Cloud Kicks has built a highly curated semantic data model (SDM) in Tableau Next containing all of its business rules, metric definitions, and generative AI field descriptions. However, another team of analysts want to use this exact same business logic to build custom visualizations in Tableau Cloud. What should the Tableau Next Consultant recommend to this team?
Answer: B
Explanation:
The correct approach is to use the Tableau Semantics connector from Tableau Cloud. Salesforce explicitly supports connecting Tableau Desktop and Tableau Cloud directly to semantic models defined in Data 360
/Tableau Semantics. This allows downstream Tableau authors to consume the same governed measures, dimensions, calculations, relationships, metrics, and business definitions that Tableau Next uses.
Option B would undermine the principal benefit of the semantic layer. Reconnecting directly to Data 360 objects and recreating joins would force analysts to reconstruct business logic and could lead to inconsistent calculations or relationships across analytical applications. Option C is also incorrect because Data Kits are Salesforce/Data 360 deployment mechanisms; they are not used to install semantic models into Tableau Cloud as independent Tableau data models.
This interoperability capability is strategically important: Tableau Semantics provides one governed analytical definition layer that can be consumed across Tableau Next, Tableau Cloud, Tableau Desktop, AI experiences, and other supported Salesforce applications. Analysts can therefore create custom Tableau Cloud visualizations without duplicating semantic logic.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Semantics Connector - > Tableau Cloud - > Reusable Semantic Models.
NEW QUESTION # 67
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