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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Troubleshoot Calculations and Workbooks | 40% | - Optimize workbook performance
|
| Topic 2: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
| Topic 3: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
| Topic 4: Evaluate Current State | 22% | - Map current state of analytics to future state
|
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The Salesforce Analytics-Con-202 certification exam offers a great opportunity to advance your career. With the Salesforce Certified Tableau Next Consultant certification exam beginners and experienced professionals can demonstrate their expertise and knowledge. After passing the Salesforce Certified Tableau Next Consultant (Analytics-Con-202) exam you can stand out in a crowded job market. The Analytics-Con-202 certification exam shows that you have taken the time and effort to learn the necessary skills and have met the standards in the market.
NEW QUESTION # 85
Which statement accurately describes a parameter within a Tableau Next semantic model?
Answer: B
Explanation:
A parameter is a user-controllable variable that can influence calculations, filtering, reference lines, and other interactive analytical behavior. Salesforce documents parameters as a mechanism for modeling scenarios and allowing dashboard users to provide inputs that affect analytical results. Parameters can be created for different data types and then referenced by calculated fields or dashboard controls.
For example, an organization could define a parameter named Target Discount and allow a user to enter 15%.
A calculated field could then compare actual discount values with the parameter and dynamically change the resulting visualization. Tableau Next also supports parameter widgets that accept manually entered values or derive dynamic values from dashboard queries.
Option B incorrectly describes semantic-model relationship configuration. Relationships between DMOs are defined through relationship metadata and cardinality rather than parameters. Option C confuses parameters with governance and authorization controls.
The certification distinction is: parameters provide variable analytical input; calculated fields consume that input; semantic relationships define object connectivity; Data 360 governance controls security.
References/Topics: Data Setup - > Semantic Models - > Parameters - > Calculated Fields - > Interactive Scenario Modeling.
NEW QUESTION # 86
A Tableau Next Consultant is asked to configure semantic models for sales and service data. What is the benefit of semantic models?
Answer: B
Explanation:
The fundamental purpose of Tableau Semantics is to establish consistent, governed business definitions that can be reused across analytical and AI experiences. Salesforce describes semantic models as the place where organizations define and govern business metrics, relationships, dimensions, calculations, and familiar business terminology.
This means sales and service teams can use the same authoritative definitions rather than implementing independent calculations within each dashboard. For example, concepts such as Annual Recurring Revenue, Case Resolution Time, Customer Lifetime Value, or Gross Margin can be defined centrally and then reused across dashboards, metrics, Tableau Agent conversations, and other Data 360-powered experiences.
Option B is far too narrow. Semantic models can support efficient query generation, but their primary benefit is not merely dashboard rendering performance.
Option C is also incorrect. Tableau Next inherits RLS and other governance controls from Data 360 security policies; a semantic model does not automatically create record-level security simply by existing.
The exam principle is: Tableau Semantics = reusable, centrally governed meaning, ensuring humans, dashboards, metrics, and AI reason from the same business definitions.
References/Topics: Data Setup - > Tableau Semantics - > Semantic Models - > Single Source of Truth - > Standardized Business Logic.
NEW QUESTION # 87
A Tableau Next Consultant is asked to configure batch data transforms. Where is the output written?
Answer: A
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 # 88
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: B
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 # 89
What is a prerequisite for using generative AI to create calculated fields using multi- turn, natural language?
Answer: A
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 # 90
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