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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Setup | 20% | - Semantic Models
|
| Topic 2: Agentic Experiences | 25% | - Generative AI
|
| Topic 3: Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Topic 4: Managing Workspaces & Orgs | 10% | - Personal Org
|
| Topic 5: Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Topic 6: Basic Setup & Admin | 10% | - Agentic Analytics
|
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NEW QUESTION # 48
A Tableau Next Consultant has developed a dashboard using a single semantic model. The dashboard includes filters, metrics, and visualizations. The consultant wants to be able to make changes to their filters and selections in their visualizations that update the metrics. They are already able to filter visualizations between each other by clicking into the views and using the filters on the page. What does the consultant need to validate to have the desired functionality?
Answer: A
Explanation:
For a Tableau Next Metric widget to respond dynamically to dashboard filtering, two conditions must be satisfied. First, the metric widget must be configured to receive dashboard filters. Second, the dimensions being used by those dashboard filters must exist in the metric definition as supported dimensions. Salesforce specifically documents that a Metric widget can only be filtered by dimensions explicitly included in the metric definition, typically as Additional Dimensions.
Therefore, C is the only complete answer.
Option A fails because enabling dashboard-filter reception is not enough if the metric definition lacks the necessary dimensions. Tableau Next cannot apply a filter against a dimensional grain the metric does not expose. Option B has the required dimensions but explicitly disables receipt of dashboard filters, preventing the widget from responding to dashboard interactions.
This distinction is important because visualizations and metric widgets have different interaction requirements. A dashboard visualization can participate naturally in interactive filtering, whereas a governed metric must expose the appropriate dimensions in its semantic definition before those dashboard selections can alter the displayed metric result.
References/Topics: Visualizations and Dashboards - > Metric Widgets - > Receive Dashboard Filters - > Metric Additional Dimensions.
NEW QUESTION # 49
A Tableau Next Consultant is asked to configure agent scoping. What is the purpose?
Answer: A
Explanation:
Agent Scoping determines which approved semantic models Tableau Agent can search when a user's question lacks sufficient analytical context. Salesforce describes Agent Scoping as defining a fallback list of semantic models for a particular analytics agent. This fallback is used when Tableau Agent cannot infer the appropriate model from the current page or analytical asset.
Examples include asking Tableau Agent a question from Slack or embedding the agent on a page that contains no Tableau Next metric or dashboard. In those circumstances, the agent uses its approved fallback models to determine the appropriate semantic context.
When a user asks a question from a dashboard or metric that already supplies clear analytical context, Salesforce states that Tableau Agent uses the semantic models associated with that asset instead of the fallback list. User permissions remain enforced at runtime; placing a model on the fallback list never grants unauthorized access.
Option B is incorrect because Agent Scoping does not enforce row-level security; Data 360 governance and user permissions perform that function. Option C is unrelated to agent routing.
References/Topics: Agentic Experiences - > Tableau Agent - > Agent Scoping - > Fallback Semantic Models
- > Context Resolution.
NEW QUESTION # 50
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: C
Explanation:
Tableau Agent conversational analytics is delivered through the Analytics and Visualization agent template.
Salesforce's current documentation states that enabling Tableau Agent makes the Analytics and Visualization template and its prebuilt capabilities available in Agentforce Builder or Agentforce Studio. The consultant then creates an agent from that template and includes the Data Analysis subagent to enable conversational analytical responses and generated visualizations.
Therefore, B is correct.
Einstein Discovery Prediction is associated with predictive analytics and is not the preconfigured framework for Tableau Agent's conversational analytics. Tableau Next Marketplace contains reusable analytical assets and templates but is not the mechanism for constructing the core Analytics and Visualization agent.
References/Topics: Agentic Experiences - > Tableau Agent - > Analytics and Visualization Template - > Data Analysis Subagent - > Conversational Analytics.
NEW QUESTION # 51
A Tableau Next Consultant is asked to configure batch data transforms. Where is the output written?
Answer: B
Explanation:
Data Lake Object (DLO) is the correct answer among the available choices. A Data 360 Batch Data Transform requires an Output node where the transformed results are persisted. Salesforce documents that a Batch Data Transform can write its results to a new or existing target Data Lake Object, which can subsequently serve as input to identity resolution, calculated insights, segmentation, semantic modeling, or additional transforms.
A Data Stream Object is not the destination used to persist transformed output. Data streams provide ingestion pipelines that bring source data into Data 360. A Semantic Data Model is also not the physical output storage target; Tableau Semantics consumes governed Data 360 objects to provide measures, dimensions, relationships, calculations, and business context.
References/Topics: Data Setup - > Data 360 - > Batch Data Transforms - > Output Nodes - > Data Lake Objects.
NEW QUESTION # 52
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 # 53
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