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Salesforce Analytics-Con-202 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Evaluate Current State22%- Evaluate current data structures
  • 1. Assess whether existing data supports business needs
    • 2. Evaluate data lineage and performance risks
      - Map current state of analytics to future state
      • 1. Recommend Tableau Server vs Tableau Cloud including migration
        • 2. Map business needs to Tableau capabilities
          • 3. Translate analytical requirements using best practices
            Topic 2: Establish Governance and Support Published Content16%- Apply governance strategy
            • 1. Map governance requirements to Tableau capabilities
              • 2. Security, data quality, and certification strategy
                - Deploy and manage content lifecycle
                • 1. Workbook lifecycle and maintenance
                  • 2. Content distribution and publishing strategy
                    Topic 3: Design and Troubleshoot Calculations and Workbooks40%- Optimize workbook performance
                    • 1. Leverage performance recordings and caching
                      • 2. Identify and resolve performance bottlenecks
                        - Implement and optimize calculations
                        • 1. Level of Detail (LOD) expressions
                          • 2. Table calculations, date functions, and aggregations
                            - Design visualizations and advanced analytics
                            • 1. Recommend advanced chart types and densification
                              • 2. Apply Tableau order of operations
                                Topic 4: Plan and Prepare Data Connections22%- Plan for data transformation
                                • 1. Recommend appropriate data transformation strategy
                                  • 2. Specify granularity requirements
                                    - Design row-level security (RLS) and advanced connections
                                    • 1. Recommend connection methods and Tableau Bridge
                                      • 2. Implement RLS and entitlement structures

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                                        Salesforce Certified Tableau Next Consultant Sample Questions (Q78-Q83):

                                        NEW QUESTION # 78
                                        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: A

                                        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 # 79
                                        A Tableau Next Consultant needs to migrate a newly developed Tableau Next semantic model and its associated dashboards from a sandbox to the production environment. The two environments are part of the same Salesforce organization hierarchy. Which deployment method should the consultant use to natively move these assets?

                                        Answer: A

                                        Explanation:
                                        Among the listed choices, Inbound and Outbound Change Sets are the appropriate native Salesforce deployment mechanism. Tableau Next supports Salesforce Metadata API-based deployment capabilities, including change sets, for moving Tableau Next workspaces, visualizations, and dashboards between supported sandbox and target organizations. The workflow uses an outbound change set in the source org and an inbound change set in the target org.
                                        The Data 360 Ingestion API is intended for bringing data into Data 360 rather than lifecycle deployment of Tableau Next metadata. The Tableau Cloud Site Migration Tool pertains to Tableau Cloud site migration and is not the Salesforce metadata deployment mechanism described here.
                                        References/Topics: Managing Workspaces and Orgs - > Organize and Deploy Analytical Assets - > Deploy Tableau Next Assets.


                                        NEW QUESTION # 80
                                        During a Tableau Next implementation, a Tableau Next Consultant is explaining to the admin team when Agent Scoping for Tableau Agent actually takes effect. When is Agent Scoping used?

                                        Answer: A

                                        Explanation:
                                        Agent Scoping is specifically designed for situations in which Tableau Agent receives a question without clear analytical context. Salesforce defines Agent Scoping as the administrator-controlled list of semantic models that Tableau Agent can search when context cannot otherwise determine which model should answer the user's question.
                                        Typical examples include Tableau Agent being used in Slack or embedded on a page that does not contain a Tableau Next dashboard, metric, or other asset capable of supplying context. The configured semantic-model list serves as a fallback list. Tableau Agent can then determine which approved model should be used to resolve the user's request.
                                        Agent Scoping does not run indiscriminately for every question, which eliminates B. When Tableau Agent is invoked from an analytical surface that already provides clear semantic context, that existing context guides the interaction. C is also overly restrictive; Agent Scoping is not limited to metric-details pages or scenarios involving multiple models attached to a metric.
                                        Administrators can configure approved fallback semantic models for active Analytics and Visualization agents using the Data Analysis subagent.
                                        References/Topics: Agentic Experiences - > Tableau Agent - > Agent Scoping - > Fallback Semantic Models
                                        - > Context Resolution.


                                        NEW QUESTION # 81
                                        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 # 82
                                        A Tableau Next Consultant needs to migrate a newly developed Tableau Next semantic model and its associated dashboards from a sandbox to the production environment. The two environments are part of the same Salesforce organization hierarchy. Which deployment method should the consultant use to natively move these assets?

                                        Answer: A


                                        NEW QUESTION # 83
                                        ......

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