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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
| Topic 2: Evaluate Current State | 22% | - Map current state of analytics to future state
|
| Topic 3: Design and Troubleshoot Calculations and Workbooks | 40% | - Design visualizations and advanced analytics
|
| Topic 4: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
成千上萬的IT考生通過使用我們的產品成功通過考試，Salesforce Analytics-Con-202考古題質量被廣大考試測試其是高品質的。我們從來不相信第二次機會，因此給您帶來的最好的Salesforce Analytics-Con-202考古題幫助您首次就通過考試，并取得不錯的成績。VCESoft網站幫助考生通過Analytics-Con-202考試獲得認證，不僅可以節約很多時間，還能得到輕松通過Analytics-Con-202考試的保證，這是IT認證考試中最重要的考試之一。
問題 #31
How should a Tableau Next Consultant handle unjoined data objects to ensure an agent can confidently interpret the semantic data model and avoid generating invalid responses?
答案：C
解題說明：
The semantic model should form a single coherent connected cluster whenever the objects are intended to participate in the same analytical domain. Therefore, the consultant should establish valid relationships for the disconnected objects or remove them from the model when they do not belong.
Salesforce's current AI-readiness guidance explicitly warns about island tables and isolated objects.
Disconnected objects can confuse both users and Tableau Agent because a query can imply relationships that the model has not defined. Salesforce recommends ensuring that all relevant objects are part of a single connected cluster and avoiding ambiguous or unjoined objects that could cause invalid agent responses.
Leaving objects isolated, as proposed by A, therefore preserves the exact modeling condition that decreases agent confidence. Option B is also architecturally incorrect. Business Preferences provide business terminology and interpretation guidance; they are not a substitute for structural relationships between data objects.
This principle is especially important for Tableau Agent because its semantic query generation relies on the relationship graph to determine valid navigation paths between dimensions and measures. A connected, unambiguous model substantially reduces unsupported joins and misleading answers.
References/Topics: Data Setup - > Semantic Model AI Readiness - > Connected Model Structure - > Relationships - > Remove Ambiguous Objects.
問題 #32
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?
答案：C
解題說明：
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.
問題 #33
Universal Containers wants to allow sales reps to ask natural language questions about their pipeline directly from Salesforce mobile. Which configuration is required?
答案：B
解題說明：
The correct requirement is to enable the conversational analytics capability represented by Agentforce for Analytics, now called Tableau Agent. Salesforce explicitly lists the Salesforce Mobile app as a supported surface for Tableau Agent with the Data Analysis subagent. Users can ask natural-language analytical questions within Salesforce and receive grounded answers and visualizations based on their semantic models.
The terminology in option A is older but still maps directly to the current capability. Salesforce states that, beginning in July 2026, Agentforce for Analytics in Tableau Next is called Tableau Agent, while Concierge:
Analytics Q & A became the conversational analytics capability enabled by Data Analysis.
No custom Lightning component is required merely to expose this supported conversational experience on Salesforce mobile. Likewise, deploying traditional dashboards to mobile would enable dashboard consumption but would not itself provide the requested natural-language Q & A experience.
References/Topics: Agentic Experiences - > Tableau Agent - > Data Analysis - > Salesforce Mobile - > Conversational Analytics.
問題 #34
A Tableau Next Consultant is receiving complaints from end users about slow dashboard load times for a frequently used dashboard. The consultant is looking for an initial short- term solution before looking into longer term architectural changes. Which action should the consultant take to improve load times?
答案：B
解題說明：
Enable Query Cache is the appropriate short-term performance optimization for a frequently accessed Tableau Next dashboard. Query caching reduces repeated execution of expensive live analytical queries by temporarily reusing previously calculated query results.
Salesforce specifically positions dashboard query caching as a mechanism to accelerate page load times for frequently visited dashboards. Current Tableau Next functionality can store and reuse query results for up to approximately 30 minutes, thereby reducing system overhead and improving the end-user experience.
This is precisely appropriate when the consultant wants a rapid tactical improvement before investigating deeper issues such as semantic-model complexity, source performance, query design, data architecture, or excessive dashboard density.
Disabling Reflow concerns dashboard layout behavior and responsive presentation rather than query- processing latency. Turning off Tableau Agent likewise does not address the underlying query execution performed to render dashboard widgets.
Caching does involve a freshness tradeoff because viewers may temporarily receive cached rather than newly executed query results. For dashboards requiring immediately current information, live mode may still be necessary. The consultant should therefore treat caching as a deliberate performance-versus-freshness decision.
References/Topics: Visualizations and Dashboards - > Dashboard Performance - > Query Cache - > Cached Data Mode.
問題 #35
A Tableau Next Consultant needs to ensure that metrics created in Tableau Next are consistently defined across multiple business units. Which feature supports this requirement?
答案：B
解題說明：
Semantic Data Models are specifically intended to establish standardized, reusable definitions for analytical concepts and metrics. Salesforce describes Tableau Semantics as a semantic layer that maps data to familiar business terminology and standardized logic so information can be interpreted consistently across Salesforce.
Semantic models become a single source of truth for metrics, calculations, relationships, dimensions, and other analytical definitions.
Therefore, if several business units must calculate and interpret a metric consistently, the correct architecture is to define that KPI through the governed semantic model rather than recreating logic independently in each dashboard or workspace.
Workspace-level color palettes address visual presentation and branding rather than analytical definitions.
Dashboard templates can standardize dashboard structure and reuse assets, but they do not provide the authoritative semantic definition layer required to guarantee that the same business metric has the same meaning everywhere.
Salesforce explicitly states that semantic models are first-class Salesforce metadata and can power analytics and data-driven experiences throughout Data 360 and Tableau Next. This reusable semantic architecture prevents divergent metric logic from developing between departments.
References/Topics: Data Setup - > Tableau Semantics - > Semantic Data Models - > Metrics - > Standardized Business Logic - > Single Source of Truth.
問題 #36
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