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| Section | Weight | Objectives |
|---|---|---|
| Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Data Setup | 20% | - Data Architecture and Processing
|
| Basic Setup & Admin | 10% | - Agentic Analytics
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Cross-Platform Workflows
|
| Managing Workspaces & Orgs | 10% | - Workspace and Asset Management
|
| Agentic Experiences | 25% | - Agentic Readiness
|
今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。Analytics-Con-202認定試験はSalesforceの中に重要な認証試験の一つですが、JpexamにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってSalesforce Analytics-Con-202「Salesforce Certified Tableau Next Consultant」認証試験に参加する方に対して問題集を研究続けています。
質問 # 80
Before launching a dashboard in Tableau Next, a Tableau Next Consultant gives the appropriate team View access to the dashboard's workspace. Which other access consideration should the consultant verify before rolling this dashboard out to end users?
正解：A
解説：
Tableau Next asset sharing and Data 360 data access are separate layers. Granting users Viewer access to a workspace can provide inherited access to its dashboards, visualizations, and semantic models, but it does not automatically grant access to the underlying Data 360 data. Salesforce explicitly states that access to a semantic model requires access to the data space containing the model as well as access to the objects and fields used by that model.
Therefore, C is correct.
End users do not require Edit access to underlying Data 360 objects merely to view dashboard data, making A unnecessarily privileged. Similarly, B is incorrect because Consumer-level permission sets can view Tableau Next assets; Platform Analyst or Self-Service Analyst permissions are not universally required for dashboard consumption.
The architecture can be understood as three layers: license/permission access, Tableau Next asset sharing, and Data 360 data visibility/governance. Successful dashboard rollout requires all applicable layers to authorize the user. Sharing alone cannot override an administrator's data-space, DMO, DLO, field, or policy restrictions.
References/Topics: Managing Workspaces and Orgs - > Asset Sharing - > Data Spaces - > Data 360 Governance - > Semantic Model Access.
質問 # 81
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?
正解：B
解説：
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.
質問 # 82
A Tableau Next Consultant has created a new semantic model and wants to identify potential metadata gaps that might confuse the Analytics Agent. Which tool provides an automated "Readiness Score" and actionable suggestions to improve the model's clarity for the AI?
正解：A
解説：
Optimize Model is the feature designed to assess semantic models for AI readiness and recommend improvements. Salesforce describes Semantic Model AI Optimization as evaluating model quality, diagnosing problems, and supplying guided remediation that improves reliability for Tableau Agent. After the feature is enabled, an Optimize Model indicator appears in Semantic Model Builder and shows the model's AI-readiness strength.
Opening the Optimize Model panel provides a breakdown of AI-readiness indicators together with recommended corrective actions. The rating is recalculated as the semantic model changes, allowing consultants to iteratively improve metadata, structure, and agent compatibility.
Q & A Calibration serves a different purpose: it tests representative natural-language questions and helps calibrate how Tableau Agent responds. Business Preferences provide explicit business context and rules, such as how ambiguous terminology should be interpreted. Neither performs the automated holistic readiness evaluation described in the question.
References/Topics: Data Setup - > Semantic Model AI Optimization - > Optimize Model - > AI-Readiness Strength - > Recommended Actions.
質問 # 83
A financial services firm requires its Analytics Agent to consistently provide concise responses and format all currency outputs in Great British Pounds (GBP). Where should a Tableau Next Consultant configure these global formatting and output style rules?
正解：B
解説：
Business Preferences are the semantic-model mechanism for supplying organization-specific instructions and contextual guidance to Tableau Agent. They can define how the analytics agent should interpret business concepts, terminology, and preferred response conventions. Salesforce recommends keeping each Business Preference short, focused, and high-value because these instructions are added to the agent's prompt.
Accordingly, preferences such as "Use GBP when presenting monetary values" or "Keep responses concise" belong in Business Preferences rather than individual field descriptions. A field description explains what a particular semantic field means; it is not the proper location for broadly applicable output behavior. The Einstein Trust Layer addresses AI trust, security, privacy, and governance controls rather than organization- specific response formatting.
References/Topics: Agentic Experiences - > Business Preferences - > Organizational Terminology - > Response Guidance - > Semantic Model Context.
質問 # 84
Cloud Kicks stores historical customer transaction data inside Snowflake. The marketing team wants to track campaign performance in Tableau Next. However, IT compliance strictly prohibits creating duplicate copies of this dataset due to storage costs and security synchronization risks. Which architectural feature should a Tableau Next Consultant implement to meet these requirements?
正解：C
解説：
Zero Copy Data Federation is the correct architectural approach because it allows Tableau Next and Data 360 to work with Snowflake-hosted data without creating a separate duplicated dataset. The Snowflake tables remain in the source platform while the federation layer exposes them for governed analytical use. This directly addresses both storage-cost concerns and the compliance risk associated with maintaining synchronized copies.
Option B would move the data into Salesforce custom objects through an integration flow, which creates another persisted copy and increases synchronization and governance overhead. Option C also creates replicated data through scheduled batch processing and therefore violates the stated requirement to avoid duplication. Zero-copy access keeps the source system authoritative while making the dataset available for semantic modeling and Tableau Next analysis.
The important architecture pattern is federation rather than extraction: connect Snowflake, expose the required federated data objects, apply the necessary governance, and then build the Tableau Next semantic layer over those objects. This minimizes data movement while preserving analytical accessibility.
References/Topics: Data Setup - > Data 360 - > Snowflake - > Zero Copy Data Federation - > Tableau Next.
質問 # 85
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SalesforceのAnalytics-Con-202試験準備は、テストヒット率が高いため、98％〜100％の合格率です。 したがって、当社のAnalytics-Con-202学習教材は効果的であるだけでなく、有用でもあります。 誰もが知っているように、時間は誰にとっても非常に重要です。 一部の候補者は、自分の仕事や家族で非常に忙しいです。 Analytics-Con-202試験の審査に時間をかけることは非常に困難です。 ただし、Analytics-Con-202試験の教材を使用する場合、学習する時間はほとんどなく、Salesforce Certified Tableau Next Consultant合格率は高くなります。 Analytics-Con-202学習教材はあなたの信頼に値します。
Analytics-Con-202学習指導: https://www.jpexam.com/Analytics-Con-202_exam.html