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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic Setup & Admin | 10% | - Agentic Analytics
|
| Topic 2: Managing Workspaces & Orgs | 10% | - Personal Org
|
| Topic 3: Data Setup | 20% | - Data Architecture and Processing
|
| Topic 4: Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Topic 5: Agentic Experiences | 25% | - Agentic Readiness
|
| Topic 6: Embedding, Cross-Cloud, & Interoperability | 20% | - Developer Tools and APIs
|
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NEW QUESTION # 57
A Tableau Next Consultant is asked to configure agent scoping. What is the purpose?
Answer: B
Explanation:
Agent Scoping determines which approved semantic models Tableau Agent can search when a user's question lacks sufficient analytical context. Salesforce describes Agent Scoping as defining a fallback list of semantic models for a particular analytics agent. This fallback is used when Tableau Agent cannot infer the appropriate model from the current page or analytical asset.
Examples include asking Tableau Agent a question from Slack or embedding the agent on a page that contains no Tableau Next metric or dashboard. In those circumstances, the agent uses its approved fallback models to determine the appropriate semantic context.
When a user asks a question from a dashboard or metric that already supplies clear analytical context, Salesforce states that Tableau Agent uses the semantic models associated with that asset instead of the fallback list. User permissions remain enforced at runtime; placing a model on the fallback list never grants unauthorized access.
Option B is incorrect because Agent Scoping does not enforce row-level security; Data 360 governance and user permissions perform that function. Option C is unrelated to agent routing.
References/Topics: Agentic Experiences - > Tableau Agent - > Agent Scoping - > Fallback Semantic Models
- > Context Resolution.
NEW QUESTION # 58
A Tableau Next Consultant has shared a workspace with a group of users and granted them Editor access at the workspace level. However, one of the users reports that they are still unable to edit a dashboard within that workspace. What is the most likely reason for this issue?
Answer: B
Explanation:
Tableau Next applies both asset-sharing permissions and platform permission sets. Granting Editor access through workspace sharing cannot elevate a user's capabilities beyond those granted by the administrator.
Salesforce explicitly states that sharing doesn't override permissions set by the admin and that a user who lacks the permission set required to edit Tableau Next assets cannot edit an asset even when granted Editor access.
Therefore, C is correct.
Workspace-level access is designed to simplify administration. Users and Analytics Groups can be granted Viewer or Editor access to a workspace, with appropriate access applying to contained assets subject to underlying security and permission requirements. Consequently, B is incorrect as a general explanation.
Option A is also incorrect. Workspace owners can share assets subject to their own permissions, but inability of the affected user to edit is not because dashboards universally require a separate owner-assigned Editor permission.
Think of Tableau Next authorization as multiple gates: license - > Tableau Next permission set - > asset sharing - > underlying data access. A more permissive setting at a downstream sharing layer cannot bypass a restrictive upstream administrative permission.
References/Topics: Managing Workspaces and Orgs - > Workspace Sharing - > Editor Access - > Tableau Next Permission Sets - > Permission Inheritance.
NEW QUESTION # 59
Which statement accurately describes a parameter within a Tableau Next semantic model?
Answer: A
Explanation:
A parameter is a user-controllable variable that can influence calculations, filtering, reference lines, and other interactive analytical behavior. Salesforce documents parameters as a mechanism for modeling scenarios and allowing dashboard users to provide inputs that affect analytical results. Parameters can be created for different data types and then referenced by calculated fields or dashboard controls.
For example, an organization could define a parameter named Target Discount and allow a user to enter 15%.
A calculated field could then compare actual discount values with the parameter and dynamically change the resulting visualization. Tableau Next also supports parameter widgets that accept manually entered values or derive dynamic values from dashboard queries.
Option B incorrectly describes semantic-model relationship configuration. Relationships between DMOs are defined through relationship metadata and cardinality rather than parameters. Option C confuses parameters with governance and authorization controls.
The certification distinction is: parameters provide variable analytical input; calculated fields consume that input; semantic relationships define object connectivity; Data 360 governance controls security.
References/Topics: Data Setup - > Semantic Models - > Parameters - > Calculated Fields - > Interactive Scenario Modeling.
NEW QUESTION # 60
What is a prerequisite for using generative AI to create calculated fields using multi- turn, natural language?
Answer: B
Explanation:
Option A, Draft with Einstein, can generate a calculation from natural language, but the question specifically asks about the multi-turn conversational agent experience, where the agent interacts with the user and refines the requirement. Therefore it is not the best answer. Option B addresses optimization/readiness rather than calculated-field authoring.
References/Topics: Agentic Experiences - > Tableau Agent - > Semantic Modeling/Data Pro - > Conversational Calculated Fields - > Natural-Language Authoring.
NEW QUESTION # 61
A Tableau Next Consultant needs to migrate a newly developed Tableau Next semantic model and its associated dashboards from a sandbox to the production environment. The two environments are part of the same Salesforce organization hierarchy. Which deployment method should the consultant use to natively move these assets?
Answer: A
NEW QUESTION # 62
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