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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Setup | 20% | - Semantic Models
|
| Topic 2: Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Topic 3: Agentic Experiences | 25% | - Agentic Readiness
|
| Topic 4: Managing Workspaces & Orgs | 10% | - Personal Org
|
| Topic 5: Basic Setup & Admin | 10% | - Agentic Analytics
|
| Topic 6: Embedding, Cross-Cloud, & Interoperability | 20% | - Tableau Next Apps and Marketplace
|
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NEW QUESTION # 16
What is a prerequisite for using generative AI to create calculated fields using multi- turn, natural language?
Answer: C
Explanation:
Option A, Draft with Einstein, can generate a calculation from natural language, but the question specifically asks about the multi-turn conversational agent experience, where the agent interacts with the user and refines the requirement. Therefore it is not the best answer. Option B addresses optimization/readiness rather than calculated-field authoring.
References/Topics: Agentic Experiences - > Tableau Agent - > Semantic Modeling/Data Pro - > Conversational Calculated Fields - > Natural-Language Authoring.
NEW QUESTION # 17
A Tableau Next Consultant is adding a field description to a new calculated field. What are characteristics of a good field description?
Answer: C
Explanation:
A high-quality semantic field description should be clear, concise, and business-contextual, making C correct.
Tableau Agent relies heavily on semantic metadata to determine what fields represent, when they should be used, and how they relate to a user's natural-language question.
Salesforce's Tableau Next semantic-model guidance explicitly recommends descriptions that are clear and concise and add context. Effective descriptions explain the field's purpose, business scope, meaning, relevant data behavior, and relationships when appropriate. Salesforce also recommends avoiding vague definitions or descriptions that simply repeat the field label.
Option A overemphasizes length and technical calculation detail. Excessively long descriptions can introduce unnecessary complexity and ambiguity; the objective is useful business meaning, not exhaustive implementation documentation. Option B captures brevity but omits the most important factor: business context.
For example, a field named Net_Revenue is better described as "Revenue from finalized product sales after discounts, returns, and allowances" than merely "Calculated net revenue." The former gives the agent information needed to map business-language requests accurately.
References/Topics: Data Setup - > Semantic Model AI Readiness - > Field Metadata - > Business-Friendly Descriptions.
NEW QUESTION # 18
Cloud Kicks (CK) has created Tableau Next dashboards and visualizations in a sandbox. These assets rely on new semantic models also developed in the same sandbox. CK's Tableau Next Consultant plans to use a data kit to deploy all assets to the production org. During the data kit creation process, they added the dashboards and visualizations. Which step must the consultant perform to ensure the deployment succeeds?
Answer: B
Explanation:
When Tableau Next assets and their semantic-model dependencies are deployed through a Data Kit, the consultant must explicitly review and correct the publishing sequence. Salesforce documents a minimum dependency sequence of Semantic Models - > Workspaces - > Visualizations - > Dashboards. This ordering is mandatory because visualizations depend on both semantic models and workspaces, while dashboards depend on their visualizations. Salesforce specifically warns that deployment can fail if the publishing sequence isn't reviewed and updated appropriately.
Selecting DevOps is part of creating the deployment Data Kit, but it does not automatically resolve every dependency sequence, making B insufficient. Salesforce notes that the default sequence can be based on asset creation dates and therefore might not match the required dependency order. A is also incorrect because semantic models and Tableau Next assets can be included in a Data Kit deployment to a production home org; deploying the semantic model separately through a change set is not the required approach.
References/Topics: Managing Workspaces and Orgs - > Data Kits - > DevOps Deployment - > Publishing Sequence - > Dependency Management.
NEW QUESTION # 19
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 # 20
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
NEW QUESTION # 21
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