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| Section | Weight | Objectives |
|---|---|---|
| Managing Workspaces & Orgs | 10% | - Workspace and Asset Management
|
| Basic Setup & Admin | 10% | - Tableau Next Setup
|
| Data Setup | 20% | - Semantic Models
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Tableau Next Apps and Marketplace
|
| Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Agentic Experiences | 25% | - Analytics Agent
|
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NEW QUESTION # 35
A Tableau Next Consultant interacts with a Tableau Next dashboard and identifies an issue with a Tableau Next metric: It's using the wrong date field to trend the measure field. The consultant has confirmed that the correct date field exists within the semantic model. How should the consultant update the Tableau Next metric?
Answer: C
Explanation:
The consultant should modify the metric definition in the underlying semantic model. Tableau Next metrics are semantic-layer objects. Salesforce defines a metric as a business KPI derived from one or more measures together with a date field, and metrics are built from semantic models.
Therefore, selecting which date dimension governs the metric's time trend is part of the metric's semantic definition rather than dashboard presentation logic. The dashboard consumes the governed metric; changing the dashboard would not correct the authoritative metric definition.
Option B is also inappropriate. Data Lake Objects and Data Model Objects define the underlying physical
/logical data available in Data 360. Because the correct date field already exists in the semantic model, there is no requirement to modify the underlying DLO or DMO. The problem lies specifically in which existing semantic field the metric uses for its temporal definition.
This reflects a central Tableau Next architectural principle: reusable analytical business logic-including metrics, calculations, temporal semantics, and contextual definitions-should reside in Tableau Semantics, while dashboards remain downstream consumption and presentation layers.
References/Topics: Data Setup - > Semantic Models - > Metrics - > Measure and Date Field Configuration.
NEW QUESTION # 36
A Tableau Next Consultant shares a metric in Slack that contains sensitive data governed by Data 360 policies. A user who does not have a Tableau Next license views the shared message in Slack and can see the metric image. The consultant is concerned there's a problem with the data security setup. Which statement correctly describes this behavior?
Answer: C
Explanation:
For a standard Tableau Next Slack message preview, Salesforce explicitly warns that anyone who can see the Slack message can see its preview, including filters applied when it was shared. Consequently, users should verify that the preview does not expose inappropriate sensitive information before posting it into a channel or conversation.
Tableau Next's Slack integration generates previews according to the sender's permission access and Tableau Next governance context. Once that snapshot-style preview is shared, visibility of the message governs who can see that static representation.
Therefore, C correctly describes the behavior. There is no indication that Data 360 security has malfunctioned.
Option A is wrong because recipients do not automatically obtain unrestricted interactive access to the underlying Tableau Next asset. Option B incorrectly assumes that each static Slack preview is dynamically authorization-checked as though the recipient were opening Tableau Next.
A critical distinction is that Slack Canvas cards behave differently: Canvas previews can refresh using each viewer's Tableau Next access, and unauthorized users cannot see the live underlying content.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next in Slack - > Shared Previews - > Governance.
NEW QUESTION # 37
A Tableau Next Consultant wants to enable conversational analytics for marketing data stored in Snowflake.
What is required?
Answer: A
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 # 38
An Agentforce Sales customer approaches a Tableau Next Consultant asking for analytics on pipeline health and rep performance. They have no existing analytics investment, want minimal setup time, and have no plans to build custom dashboards. What should the consultant recommend to meet this requirement?
Answer: B
Explanation:
The Tableau Next Sales Insights app is specifically designed to provide preconfigured sales analytics covering areas such as sales performance, pipeline health, and related operational sales measures. Salesforce describes Sales Insights as a packaged, data-driven sales solution that combines Tableau Next dashboards and analytics with Salesforce customer and sales data. It is intended to accelerate deployment rather than requiring organizations to design an analytics architecture from the ground up.
That positioning matches every constraint in the scenario: the organization already uses Agentforce Sales, has no existing analytics investment, wants minimal implementation effort, and does not intend to develop custom dashboards.
A custom Lightning Web Component would increase development, testing, governance, and maintenance effort. Full Tableau Next Creator licenses would give users broader authoring functionality than the stated requirement demands and would still leave the organization responsible for creating the analytical content itself.
Sales Insights also includes packaged semantic models, data objects, metrics, and dashboards, which substantially reduces the configuration burden compared with a greenfield Tableau Next implementation.
References/Topics: Basic Setup and Admin - > Tableau Next Sales Insights - > Packaged Analytics - > Agentforce Sales Integration.
NEW QUESTION # 39
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: B
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 # 40
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