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| Section | Weight | Objectives |
|---|---|---|
| Agentic Experiences | 25% | - Analytics Agent
|
| Basic Setup & Admin | 10% | - Agentic Analytics
|
| Managing Workspaces & Orgs | 10% | - Workspace and Asset Management
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Data Setup | 20% | - Semantic Models
|
当社のウェブサイトJPNTestの購入手続きは安全です。 ダウンロード、インストール、および使用は安全であり、製品にウイルスがないことを保証します。 最高のサービスと最高のAnalytics-Con-202試験トレントを提供し、製品の品質が良好であることを保証します。 電子的なAnalytics-Con-202ガイドトレントがウイルスを増幅するのではないかと心配する人が多く、ウイルスを誤って報告する専門家ではないアンチウイルスソフトウェアを使用する人もいます。 サービスとAnalytics-Con-202学習教材はどちらも優れており、当社SalesforceのSalesforce Certified Tableau Next Consultant製品とウェブサイトはウイルスがなくても絶対に安全であると考えてください。
質問 # 66
Universal Containers is concerned about Personally Identifiable Information (PII) being exposed when using Data Connection and Analytics Creation capabilities to create calculated fields. How does Data Connection and Analytics Creation ensure data security when processing requests through the large language model (LLM)?
正解：B
解説：
The correct security mechanism is the Einstein Trust Layer's PII masking capability. Salesforce guidance for AI-assisted calculated-field creation states that the agent may use information from the semantic model, including its schema and metadata, and that personally identifiable information is masked by the Einstein Trust Layer before information is sent to the LLM.
This allows Tableau Next's generative functionality to receive the semantic context required to construct useful calculated fields while applying Salesforce's enterprise AI security controls.
Option A describes a whole-model encryption/decryption workflow that is not the documented Tableau Next processing model. Option C is also incorrect because the system does not simply prohibit every field that could potentially contain PII. Instead, the Trust Layer applies masking and other controls to protect sensitive information while retaining useful analytical context.
More broadly, Salesforce states that Tableau Agent and Agentforce inherit the Einstein Trust Layer's security, governance, and trust mechanisms, including protections designed to prevent customer data from being retained by external LLMs for model training.
References/Topics: Agentic Experiences - > Einstein Trust Layer - > PII Masking - > Generative AI Calculated Fields.
質問 # 67
A Tableau Next Consultant has shared a workspace with a group of users and granted them Editor access at the workspace level. However, one of the users reports that they are still unable to edit a dashboard within that workspace. What is the most likely reason for this issue?
正解：B
解説：
Tableau Next applies both asset-sharing permissions and platform permission sets. Granting Editor access through workspace sharing cannot elevate a user's capabilities beyond those granted by the administrator.
Salesforce explicitly states that sharing doesn't override permissions set by the admin and that a user who lacks the permission set required to edit Tableau Next assets cannot edit an asset even when granted Editor access.
Therefore, C is correct.
Workspace-level access is designed to simplify administration. Users and Analytics Groups can be granted Viewer or Editor access to a workspace, with appropriate access applying to contained assets subject to underlying security and permission requirements. Consequently, B is incorrect as a general explanation.
Option A is also incorrect. Workspace owners can share assets subject to their own permissions, but inability of the affected user to edit is not because dashboards universally require a separate owner-assigned Editor permission.
Think of Tableau Next authorization as multiple gates: license - > Tableau Next permission set - > asset sharing - > underlying data access. A more permissive setting at a downstream sharing layer cannot bypass a restrictive upstream administrative permission.
References/Topics: Managing Workspaces and Orgs - > Workspace Sharing - > Editor Access - > Tableau Next Permission Sets - > Permission Inheritance.
質問 # 68
Cloud Kicks wants to track and analyze user dashboard interactions with its Tableau Next assets for governance and adoption monitoring. Which capability should a Tableau Next Consultant activate to begin collecting user interaction data?
正解：C
解説：
User Interaction Logging is the capability used to begin collecting interaction data for governance and adoption analysis. In this scenario, Cloud Kicks needs visibility into how users interact with Tableau Next dashboard assets so administrators can evaluate usage patterns, adoption, and engagement. Enabling interaction logging creates the foundation for capturing those user events and making them available for downstream monitoring and analysis.
Tableau Next Auditing is associated with broader administrative audit and governance use cases, while Session Tracing is oriented toward tracing agent sessions and AI behavior. Neither is the specific capability identified here for initiating dashboard interaction collection. The requirement is centered on user activity against Tableau Next assets rather than agent reasoning or session diagnostics.
A consultant should distinguish telemetry collection from downstream analysis. First, interaction events must be captured through User Interaction Logging; those events can then be analyzed to understand which assets are used, how users engage with dashboards, and where adoption or governance attention is required. This supports operational oversight without changing the analytical content itself.
References/Topics: Basic Setup and Admin - > Tableau Next User Interaction Logging - > Governance and Adoption Monitoring.
質問 # 69
What is a logical view within a semantic data model?
正解：A
解説：
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.
質問 # 70
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 # 71
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短時間で一番質高いSalesforceのAnalytics-Con-202練習問題を探すことができますか？もしできなかったら、我々のAnalytics-Con-202試験資料を試していいですか？我が社のAnalytics-Con-202問題集は多くの専門家が数年間で努力している成果ですから、短い時間をかかってSalesforceのAnalytics-Con-202試験に参加できて、予想以外の成功を得られます。それで、SalesforceのAnalytics-Con-202に参加する予定がある人々は速く行動しましょう。
Analytics-Con-202資格問題集: https://www.jpntest.com/shiken/Analytics-Con-202-mondaishu