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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
| Topic 2: Evaluate Current State | 22% | - Map current state of analytics to future state
|
| Topic 3: Design and Troubleshoot Calculations and Workbooks | 40% | - Implement and optimize calculations
|
| Topic 4: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
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NEW QUESTION # 67
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 # 68
A Tableau Next Consultant needs to ensure that metrics created in Tableau Next are consistently defined across multiple business units. Which feature supports this requirement?
Answer: A
Explanation:
Semantic Data Models are specifically intended to establish standardized, reusable definitions for analytical concepts and metrics. Salesforce describes Tableau Semantics as a semantic layer that maps data to familiar business terminology and standardized logic so information can be interpreted consistently across Salesforce.
Semantic models become a single source of truth for metrics, calculations, relationships, dimensions, and other analytical definitions.
Therefore, if several business units must calculate and interpret a metric consistently, the correct architecture is to define that KPI through the governed semantic model rather than recreating logic independently in each dashboard or workspace.
Workspace-level color palettes address visual presentation and branding rather than analytical definitions.
Dashboard templates can standardize dashboard structure and reuse assets, but they do not provide the authoritative semantic definition layer required to guarantee that the same business metric has the same meaning everywhere.
Salesforce explicitly states that semantic models are first-class Salesforce metadata and can power analytics and data-driven experiences throughout Data 360 and Tableau Next. This reusable semantic architecture prevents divergent metric logic from developing between departments.
References/Topics: Data Setup - > Tableau Semantics - > Semantic Data Models - > Metrics - > Standardized Business Logic - > Single Source of Truth.
NEW QUESTION # 69
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: B
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 # 70
A data analyst at Cloud Kicks has been tasked with creating and sharing Tableau Next assets, including workspaces and dashboards. The user also needs to manage a Personal Org. They have been assigned a Tableau Next Creator license. Which permission set should a Tableau Next Consultant assign to this user to fulfill these requirements?
Answer: B
Explanation:
Salesforce guidance states that a Tableau Next Self-Service Analyst requires a Tableau Next Creator license and provides access to Tableau Next Studio, Tableau Agent, Personal Org ownership, analytical assets, and self-service creation/editing functionality within the Personal Org. Personal Org owners can create workspaces and dashboards and can share their workspace with other analysts under the Personal Org collaboration model.
Because this question expressly includes management of a Personal Org and is framed around a self-service analyst persona, C remains the correct exam-bank answer.
References/Topics: Managing Workspaces and Orgs - > Personal Org - > Tableau Next Creator - > Self- Service Analyst Permissions.
NEW QUESTION # 71
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: A
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 # 72
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