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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Troubleshoot Calculations and Workbooks | 40% | - Implement and optimize calculations
|
| Topic 2: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
| Topic 3: Evaluate Current State | 22% | - Map current state of analytics to future state
|
| Topic 4: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
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NEW QUESTION # 45
Cloud Kicks (CK) is designing a unified Customer 360 semantic data model (SDM). CK requires strict row level security (RLS) so that regional sales managers can only query and view accounts within their assigned territories. What is the recommended best practice for enforcing this security requirement in Tableau Next?
Answer: B
Explanation:
Tableau Next row-level security should be implemented through Data 360 governance policies, making B correct. Salesforce explicitly states that for Data 360 DLOs and DMOs, record-level security is defined through Data 360 policies. Tableau Next dashboards and visualizations built on those objects then inherit the enforced data visibility.
For example, the account dataset can include an attribute identifying the responsible territory or regional manager. A Data Governance access policy can evaluate the current user's relevant attributes and restrict returned records accordingly. Because security is enforced below the semantic and visualization layers, the same rules consistently protect dashboards, metrics, Tableau Agent queries, and other downstream consumers.
Option A describes the traditional Tableau USERNAME() pattern, but current Tableau Next guidance explicitly states that native user functions such as USERNAME() aren't the preferred/current RLS implementation for this architecture. Option C creates serious scalability, maintenance, and consistency problems and violates centralized governance principles.
The architectural rule is: define security as close to the governed data layer as possible so every downstream analytical experience inherits the same policy.
References/Topics: Data Setup - > Data 360 Governance - > Row-Level Security - > Access Policies - > Data Object Security.
NEW QUESTION # 46
A Tableau Next Consultant identifies an issue with the default filtering on a metric embedded on an Opportunity record page. The metric is showing data aggregated for the parent account of the Opportunity, but it should be filtered to the Opportunity level. The consultant has verified that the relevant dimensions are added to the metric definition. Which change should the consultant make to the metric?
Answer: C
Explanation:
The existing parent-account filter is causing the metric to operate at Account scope, so merely adding another filter does not correctly express the desired record context. The consultant should remove that Account-based mapping and configure the metric dimension Opportunity ID to equal the Opportunity ID of the current Salesforce record. Therefore, B is correct.
Salesforce's embedded Tableau Next components support Record Value filtering on Lightning record pages.
With Record Value, the filter dynamically retrieves a field from the Salesforce record currently being viewed and compares it against the selected field or dimension in the Tableau Next data model.
Option A fails to remove the inappropriate parent Account filter, leaving conflicting or overly restrictive context. Option C maps unlike identifiers-Opportunity ID against Account ID-which is logically invalid.
This question tests contextual embedding architecture: the metric definition must expose the required dimension, while Lightning App Builder supplies the current record value used to dynamically scope the embedded metric.
References/Topics: Visualizations and Dashboards - > Embedded Tableau Next Metrics - > Record Value Filters - > Lightning Record Context.
NEW QUESTION # 47
A new analyst at Coral Cloud Resorts is responsible for the complete setup of Tableau Next. This includes configuring the platform, managing all users, and overseeing all workspaces and assets. Which permission set must a Tableau Next Consultant assign to the analyst?
Answer: C
Explanation:
Tableau Unmetered Admin is the correct permission set because the user needs full Tableau Next administrative capability rather than analytical authoring alone. Salesforce states that Tableau Unmetered Admin permits access to Tableau Next Setup and edit capabilities across Tableau Next features in the production environment. Administrative users can configure the product and provision users with appropriate Tableau Next permission sets.
Salesforce specifically requires Tableau Unmetered Admin or Tableau Next Admin to access Tableau Next Administration and enable Tableau Next. When Tableau Next is enabled, the administrator also receives the associated Data Cloud Architect capabilities required for underlying Data 360 configuration.
The Platform Analyst role is intended primarily for creating, managing, and sharing governed Tableau Next analytical assets. It does not represent the full administrative persona required to configure Tableau Next and manage users. Self-Service Analyst focuses primarily on Personal Org and self-service analytical workflows and is even further removed from the stated administration requirement.
This question follows least-privilege role design: administrative tasks require the Admin persona; content creation alone generally requires an Analyst persona.
References/Topics: Basic Setup and Admin - > Tableau Next Permission Sets - > Tableau Unmetered Admin
- > User Management - > Tableau Next Setup.
NEW QUESTION # 48
A Tableau Next Consultant wants to implement a Tableau Agent within Tableau Next to provide conversational insights and automated chart summaries for its sales operations managers. The deployment strategy requires using a preconfigured platform framework to accelerate the setup. How should the consultant deploy this specific conversational analytics capability?
Answer: B
NEW QUESTION # 49
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 # 50
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