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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate Current State | 22% | - Evaluate current data structures
|
| Topic 2: Design and Troubleshoot Calculations and Workbooks | 40% | - Optimize workbook performance
|
| Topic 3: Establish Governance and Support Published Content | 16% | - Apply governance strategy
|
| Topic 4: Plan and Prepare Data Connections | 22% | - Plan for data transformation
|
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NEW QUESTION # 13
What is a prerequisite for using generative AI to create calculated fields using multi- turn, natural language?
Answer: B
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 # 14
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 # 15
A Tableau Next Consultant has completed all the required Salesforce setup steps for Agentforce for Analytics in Slack. However, when the consultant tests it in Slack, they're not seeing the Agentforce icon on a Tableau Next metric card. The consultant confirms that the agent is built from the Analytics and Visualization template and uses the Data Analysis subagent. What is an additional criteria that must be met for the icon to appear?
Answer: A
Explanation:
The Analytics and Visualization Agent must be installed in Slack and enabled for the workspace. Creating the agent from the correct template and including the Data Analysis subagent is necessary, but those steps alone do not expose the conversational experience on Slack metric or dashboard cards.
Salesforce documents several criteria that Slack checks before displaying the Ask Agentforce for Analytics capability. The qualifying Analytics and Visualization agent must be installed and enabled in the Slack workspace, use the Data Analysis subagent, originate from the same Salesforce org as the shared Tableau Next asset, and be accessible to the user through the relevant agent permissions.
A Personal Org is not required; in fact, conversational analytics has specific Personal Org limitations. Nor must an administrator initiate Tableau Agent on behalf of ordinary users. Properly licensed and authorized users can invoke it themselves.
This question tests the distinction between configuring an agent inside Salesforce and actually making that agent available through an external collaboration surface such as Slack.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Integrate Analytics Across Platforms -
> Tableau Next in Slack - > Enable Tableau Agent in Slack.
NEW QUESTION # 16
A Tableau Next Consultant is asked to configure row-level security (RLS) for sensitive HR data. Where should this be defined?
Answer: C
Explanation:
Row-level security for this scenario should be defined in the semantic model so the governed analytical layer determines which records each user is allowed to retrieve. By applying RLS at the semantic-model level, the access rule travels with the analytical definitions used by metrics, visualizations, dashboards, and agentic experiences instead of depending on a particular dashboard layout.
Dashboard filters and visualization properties are presentation controls. They can alter what appears on a given screen, but they are not an appropriate substitute for a security rule because a user could potentially access the same underlying semantic content through another approved analytical surface. The restriction therefore belongs in the model that governs how analytical queries resolve against the sensitive HR dataset.
For an HR scenario, the RLS rule can be based on attributes such as employee, manager, department, region, or another authorized access dimension. The objective is to ensure that every downstream analytical experience receives only the rows permitted for the signed-in user. This centralizes security behavior and reduces the risk of inconsistent filtering across dashboards.
References/Topics: Data Setup - > Semantic Models - > Row-Level Security - > Governed HR Data.
NEW QUESTION # 17
What is a logical view within a semantic data model?
Answer: B
Explanation:
A Logical View is a semantic data object that combines underlying objects through explicitly configured joins or unions and presents the resulting structure as a single analytical object. Of the choices provided, A correctly captures this concept: the view contains participating objects whose join structure and relationship characteristics must be defined.
Salesforce's current glossary defines a Logical View as a data object that combines multiple tables using special joins and then allows that enriched dataset to be queried as one object. It can subsequently participate in calculated fields, metrics, semantic relationships, and other definitions.
Proper cardinality is important because Tableau Semantics must know whether objects relate one-to-one, one- to-many, many-to-one, or many-to-many to prevent duplicate aggregation or missing results. Salesforce provides explicit cardinality configuration for semantic relationships.
Option B confuses a Logical View with ordinary semantic relationships inherited or defined among data objects. Option C describes neither the structure nor purpose of Logical Views.
The critical exam distinction is relationships preserve separate objects, while a Logical View combines objects into a single logical analytical structure.
References/Topics: Data Setup - > Tableau Semantics - > Logical Views - > Joins - > Cardinality.
NEW QUESTION # 18
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