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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Setup | 20% | - Data Architecture
|
| Topic 2: Agentic Experiences | 25% | - Generative AI
|
| Topic 3: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 4: Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Topic 5: Managing Workspaces & Orgs | 10% | - Asset Management and Sharing
|
| Topic 6: Basic Setup & Admin | 10% | - Tableau Next Activation and Management
|
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NEW QUESTION # 33
A Tableau Next Consultant wants to enable conversational analytics for marketing data stored in Snowflake.
What is required?
Answer: C
Explanation:
Tableau Agent's conversational analytics depends on an AI-ready Tableau semantic model, making A correct.
Salesforce positions Tableau Semantics as the governed business layer between connected Data 360 sources and analytical or AI consumption experiences. Semantic models define standardized measures, dimensions, relationships, calculations, metrics, terminology, and business context that Tableau Agent uses to interpret natural-language questions.
For Snowflake data, the consultant establishes an appropriate Data 360 connection-including supported zero- copy federation where appropriate-and exposes the required Snowflake data through Data 360. The consultant then builds and prepares a semantic model containing the marketing concepts that Tableau Agent must analyze. Salesforce's Tableau Agent implementation guidance explicitly requires determining the required data, connecting it through Data 360, and creating suitable semantic foundations before configuring conversational analytics.
CSV export is unnecessary and creates an avoidable duplicate dataset. Building dashboards directly in Snowflake also does not establish Tableau Agent's governed semantic context.
The architecture is therefore: Snowflake - > Data 360 connectivity - > Tableau Semantic Model - > Tableau Agent - > Conversational Analytics.
References/Topics: Data Setup - > Snowflake Integration - > Data 360 - > Tableau Semantics - > AI-Ready Semantic Models.
NEW QUESTION # 34
The client wants AI-generated insights about Gross Margin % in Tableau Next. Where is the best place to define this context for the agent?
Answer: A
NEW QUESTION # 35
A Tableau Next Consultant successfully creates a calculated field using Data Connection and Analytics Creation Subagent involving a Sales data model object (DMO) and Customer DMO. However, when attempting a similar calculation involving a Territory DMO, Data Pro fails. What is the most likely cause?
Answer: A
Explanation:
Data Pro-now increasingly referred to under Semantic Modeling functionality-depends on relationships already defined between the data model objects participating in a generated calculated field. Salesforce explicitly states that a predefined relationship must exist between DMOs for Data Pro to create a calculated field spanning those objects. The agent cannot construct a valid cross-object calculation when the required relationship does not exist.
Therefore, if calculations involving Sales and Customer work but introducing Territory causes the operation to fail, the most likely issue is that Territory is not relationally connected to the relevant objects.
Good metadata and field descriptions improve semantic clarity and AI accuracy, but insufficient descriptions are not the hard technical prerequisite being tested. Likewise, Salesforce does not document a general two- DMO maximum corresponding to option C.
The broader architectural principle is that generative semantic-model functionality does not replace proper data modeling. AI features consume the semantic model's existing object structure, metadata, and relationships. Consultants must establish valid relationships before expecting Tableau Agent to synthesize calculations involving fields from multiple objects.
References/Topics: Data Setup - > Semantic Models - > DMO Relationships - > AI-Generated Calculated Fields - > Data Pro/Semantic Modeling.
NEW QUESTION # 36
Sales managers at Universal Containers (UC) want to receive proactive data alerts for their Tableau Next metrics directly within their Slack channels. UC has already enabled the Proactive Alert setting and Agentforce for Analytics in the Salesforce org. Which additional prerequisite must be met for users to receive these alerts in Slack?
Answer: B
Explanation:
The additional requirement is to enable Slack for Tableau Next, represented by option B's Collaboration with Slack and Tableau Next setting. Salesforce's current administrative workflow requires Salesforce and Slack to be connected and the Collaborate with Slack and Tableau Next option enabled in Tableau Next Administration.
For proactive alerts specifically, Salesforce states that an administrator must enable Slack for Tableau Next and configure Agentforce in Slack. Once properly configured, Inspector Proactive Data Alerts can deliver notifications into Tableau Next and Slack when a user-defined metric condition is satisfied.
Assigning a generic "Slack User" permission set is not the documented Tableau Next prerequisite in this scenario. Users instead require an eligible Tableau Next permission set and access to the relevant asset.
Likewise, a separate analytics agent created specifically for Slack is not required merely to receive Inspector alerts.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Slack Integration - > Inspector Proactive Data Alerts - > Collaborate with Slack and Tableau Next.
NEW QUESTION # 37
A Tableau Next Consultant is advising a customer on when to use Tableau Agent versus building a traditional Tableau Next dashboard. Which scenario is the best fit for Tableau Agent?
Answer: B
Explanation:
Tableau Agent is optimized for conversational, exploratory analytics in which a business user asks natural- language questions and receives grounded answers and visualizations. A request such as, "What are my top accounts by Annual Contract Value this quarter?" maps directly to Tableau Agent's supported descriptive- analysis capabilities. Salesforce explicitly lists Top-N questions, aggregations, dimensional breakdowns, comparisons, and other business-oriented analytical questions among the supported conversational patterns.
A fixed monthly-close report for a board is better represented by a governed dashboard or reporting asset because its structure, presentation, and recurring content are predetermined. Likewise, Tableau Agent is not positioned as a general-purpose data-science workflow development environment.
The architectural distinction is important for the exam: dashboards provide curated, repeatable analytical experiences, whereas Tableau Agent adds an interactive conversational layer over semantic models. It translates the user's business question into semantic analytical operations and returns contextual text and visual output.
References/Topics: Agentic Experiences - > Analyze and Share Data in Tableau Next - > About Conversational Analytics - > Supported Questions and Surfaces.
NEW QUESTION # 38
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