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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic Setup & Admin | 10% | - Tableau Next Setup
|
| Topic 2: Data Setup | 20% | - Data Architecture and Processing
|
| Topic 3: Agentic Experiences | 25% | - Analytics Agent
|
| Topic 4: Managing Workspaces & Orgs | 10% | - Personal Org
|
| Topic 5: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 6: Embedding, Cross-Cloud, & Interoperability | 20% | - Tableau Next Apps and Marketplace
|
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NEW QUESTION # 20
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 # 21
A Tableau Next Consultant is looking to share a newly created workspace with a group of 50 end users and wants to avoid having to add users individually. Which feature should the consultant use to solve for this?
Answer: A
Explanation:
Analytics Groups provide the native Tableau Next mechanism for sharing analytical assets with multiple users as a single administrative unit. Salesforce states that Analytics Groups are specifically designed to simplify access management by allowing an administrator to create a group, add members, and then share Tableau Next assets with the entire group rather than adding each user individually.
That makes C the direct solution for sharing a workspace with 50 users.
Custom permission sets govern functional capabilities-what users are allowed to do in Tableau Next-but are not the asset-sharing construct for granting workspace access to a collection of users. Data spaces control access to Data 360 data and governance boundaries; they are not substitutes for Tableau Next workspace sharing.
After the Analytics Group has been created and populated, the workspace owner can use the normal Share interface, select the group, and grant Viewer or Editor access as appropriate. The role applied to a workspace can then be inherited by supported assets contained in that workspace, subject to each user's underlying licenses, permission sets, and data access.
References/Topics: Managing Workspaces and Orgs - > Analytics Groups - > Workspace Sharing - > Group-Based Access Management.
NEW QUESTION # 22
Cloud Kicks stores historical customer transaction data inside Snowflake. The marketing team wants to track campaign performance in Tableau Next. However, IT compliance strictly prohibits creating duplicate copies of this dataset due to storage costs and security synchronization risks. Which architectural feature should a Tableau Next Consultant implement to meet these requirements?
Answer: B
Explanation:
Zero Copy Data Federation is the correct architectural approach because it allows Tableau Next and Data 360 to work with Snowflake-hosted data without creating a separate duplicated dataset. The Snowflake tables remain in the source platform while the federation layer exposes them for governed analytical use. This directly addresses both storage-cost concerns and the compliance risk associated with maintaining synchronized copies.
Option B would move the data into Salesforce custom objects through an integration flow, which creates another persisted copy and increases synchronization and governance overhead. Option C also creates replicated data through scheduled batch processing and therefore violates the stated requirement to avoid duplication. Zero-copy access keeps the source system authoritative while making the dataset available for semantic modeling and Tableau Next analysis.
The important architecture pattern is federation rather than extraction: connect Snowflake, expose the required federated data objects, apply the necessary governance, and then build the Tableau Next semantic layer over those objects. This minimizes data movement while preserving analytical accessibility.
References/Topics: Data Setup - > Data 360 - > Snowflake - > Zero Copy Data Federation - > Tableau Next.
NEW QUESTION # 23
A Tableau Next Consultant wants to use size encoding to highlight the volume of sales for products on a scatter plot. However, once the consultant builds the chart, they realize that the marks are overlapping and high- volume selling products are obscuring lower volume products. What should the consultant do to improve the visibility of the chart while highlighting differential sales volumes?
Answer: B
Explanation:
Tableau Next provides explicit controls for size encoding, including minimum and maximum mark-size limits. With relative size encoding, Minimum (% of cell) defines the smallest percentage of available cell space that a mark can occupy, while Maximum (% of cell) controls the largest. Adjusting these settings allows the author to retain sales-volume encoding while limiting oversized marks that obscure smaller ones.
Therefore, B directly addresses the visualization problem.
A dual axis changes the structural representation of the data rather than correcting excessive mark sizing and is unnecessary for this requirement. Increasing the physical dashboard container size might provide additional display space, but it does not directly control the relative size range of the marks and cannot reliably prevent high-value marks from dominating lower-value marks.
Tableau Next supports both Relative and Absolute size modes. Relative sizing is particularly useful when the visual objective is to emphasize differences across a continuous measure while maintaining visual readability.
The consultant should tune the minimum and maximum percentage parameters until the volume relationship remains perceptible without excessive overlap.
References/Topics: Visualizations and Dashboards - > Marks - > Size Encoding - > Relative Size - > Minimum and Maximum Percent of Cell.
NEW QUESTION # 24
Universal Containers connected an external Model Context Protocol (MCP)- compliant AI client to the Tableau Next MCP server so analysts can query their data from that client. During rollout, an analyst asks the Tableau Next Consultant how data access is determined when they query through the external client instead of signing in to Tableau Next directly. Which statement best describes how the integration governs what each analyst can access?
Answer: C
Explanation:
Tableau Next MCP sessions operate using the OAuth-authenticated identity of the individual user, so B is correct. The external MCP client does not become the authoritative security boundary. Instead, Tableau Next continues to enforce Salesforce/Tableau permissions and governance while exposing analytical capabilities to the external AI environment.
Salesforce's Tableau Next MCP documentation explicitly states that MCP sessions use OAuth-authenticated users and that permissions and restrictions associated with the user's profile apply to the MCP session.
Tableau user permissions, semantic-model definitions, and Trust Layer protections therefore remain effective when the user accesses Tableau Next through an external MCP-compatible client.
Salesforce's broader hosted-MCP architecture follows the same principle: tools enforce per-user authentication and standard Salesforce security controls rather than granting universal access based on connector configuration.
This design preserves governance across interoperability boundaries: Tableau Next remains the governed analytical source, while the external client functions as another interaction surface.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Integrate Analytics Across Platforms -
> Tableau Next MCP Server - > Permissions and Governance.
NEW QUESTION # 25
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