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| Section | Weight | Objectives |
|---|---|---|
| Agentic Experiences | 25% | - Analytics Agent
|
| Managing Workspaces & Orgs | 10% | - Personal Org
|
| Data Setup | 20% | - Data Architecture and Processing
|
| Basic Setup & Admin | 10% | - Tableau Next Setup
|
| Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
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NEW QUESTION # 78
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: C
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 # 79
A Tableau Next Consultant is shadowing a client's business analyst who is building a new profitability dashboard in Tableau Next. The analyst knows exactly the business logic they need for a complex "Year- over- Year Weighted Margin" metric, but they are new to the platform and keep getting syntax errors when trying to write the formula. How does generative AI in Tableau Next assist the analyst in this scenario?
Answer: C
Explanation:
Tableau Next provides generative AI capabilities that translate a user's natural-language description of a calculation into a calculated-field formula. This directly addresses scenarios where an analyst understands the required business logic but lacks familiarity with Tableau Next formula syntax.
With Draft with Einstein, the user describes the calculation they need in natural language. Einstein can propose the field name, description, data type, aggregation type, and formula. The agentic calculated-field experience also supports conversational refinement, allowing the user to explain requirements and respond to clarification questions before reviewing the proposed field.
A batch transform, as described in A, is a data-preparation mechanism and is unrelated to helping an analyst overcome formula-authoring syntax problems. C is less accurate because the defining generative-AI capability being tested is the transformation of natural-language analytical intent into a proposed formula.
The generated output remains subject to user review before creation, preserving human control over the final semantic definition while substantially reducing formula-authoring friction.
References/Topics: Agentic Experiences - > Generative AI - > Draft with Einstein/Data Pro - > Natural- Language Calculated Fields.
NEW QUESTION # 80
A Tableau Next Consultant wants to use color to identify products in a table that have an average discount above a user set threshold in a visualization. The consultant creates a parameter on the dashboard for the user to specify the value. Which approach should the consultant take to have the table highlight respond to the value set in the parameter?
Answer: B
Explanation:
A Tableau Next parameter supplies a user-controlled value, while a calculated field translates that value into analytical logic. Therefore, the correct implementation is to create a Boolean calculated field-such as Average Discount greater than the threshold parameter-and use that calculated field for color encoding.
Salesforce's parameter workflow explicitly instructs authors to create the parameter and then create a calculated field whose formula responds to the selected parameter value. Salesforce separately documents Tableau Next table color encoding: a measure or calculated field can be placed on Color in the Marks section, allowing categories or condition results to receive appropriate colors.
Option B confuses dashboard text-widget conditional formatting with dynamic mark encoding inside the table visualization. Option C unnecessarily duplicates the visualization and introduces additional maintenance complexity when one visualization with calculated color logic is sufficient.
This pattern-Parameter - > Calculated Field - > Marks encoding-is the preferred design when visualization appearance must respond dynamically to user-provided analytical thresholds.
References/Topics: Visualizations and Dashboards - > Parameters - > Calculated Fields - > Color Encoding.
NEW QUESTION # 81
During a discovery call, a client tells their Tableau Next Consultant that they love the idea of embedding the Analytics and Visualization Agent into an external portal, but that they have three dashboards on the same page: Sales Performance, Risk Exposure, and Regional Revenue. What is true about the agent's capabilities in this scenario?
Answer: C
Explanation:
The Tableau Next Analytics Embedding SDK supports an AnalyticsAgent component with both single- context and multi-component operating modes. In Multi-Component mode, the application omits the contextConfig property. The agent then automatically tracks the embedded AnalyticsDashboard and AnalyticsMetric components present on that page.
Therefore, B directly describes the intended architecture for a portal containing multiple analytical components.
Option A describes single-context mode, where contextConfig explicitly binds the agent to one dashboard, metric, or semantic model. That mode exists but is not a platform limitation. Option C introduces a same- semantic-model restriction that the current Embedding SDK documentation does not impose. The SDK explicitly describes automatic context tracking across embedded dashboard and metric components.
This capability is particularly important in embedded analytics because it allows conversational analytics to understand the broader analytical surface rather than forcing the external application to continually rebind the agent as users move between embedded components.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next Embedding SDK - > AnalyticsAgent - > Multi-Component Mode.
NEW QUESTION # 82
A Tableau Next Consultant is demonstrating how Tableau Agent provides transparency for its responses. A user asks Tableau Agent a question and receives a response with a visualization. The user now needs to examine how the agent arrived at the response. Which action must the user take in Tableau Agent to see this information?
Answer: A
Explanation:
The user should select Sources. Tableau Agent's conversational analytics experience exposes a Sources control specifically to provide transparency into how a response was generated.
Salesforce documents that after Tableau Agent returns an analytical answer, users can select Sources to review details about how the agent analyzed the request, including the data sources and metrics used during the analysis. Salesforce also states that conversational analytics responses include information about the sources and reasoning used in the analysis, supporting traceability and user trust.
Option B concerns visualization configuration rather than agent reasoning and source grounding. Option C is not the documented Tableau Agent workflow for exposing reasoning provenance to end users.
This capability is important because Tableau Agent performs semantic queries against the underlying semantic model, synthesizes the returned data into a textual response, and generates a suitable visualization.
Sources helps users understand the analytical basis of that output rather than requiring them to accept an opaque AI-generated answer.
References/Topics: Agentic Experiences - > Tableau Agent - > Conversational Analytics - > Sources - > Response Transparency and Grounding.
NEW QUESTION # 83
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