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| Section | Weight | Objectives |
|---|---|---|
| Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Agentic Experiences | 25% | - Generative AI
|
| Managing Workspaces & Orgs | 10% | - Personal Org
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Basic Setup & Admin | 10% | - Access Management
|
| Data Setup | 20% | - Data 360
|
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NEW QUESTION # 35
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?
Answer: B
Explanation:
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.
NEW QUESTION # 36
A Tableau Next Consultant has developed a semantic model and downstream assets using the Published Data Source (PDS) connector from Tableau Cloud. Their test end user has the correct permissions and access to the workspace, semantic model, and assets, but is unable to see anything when they open the assets in Tableau Next. What is a potential reason for the lack of visibility?
Answer: A
Explanation:
A semantic model created from a Tableau Cloud Published Data Source remains directly connected to the PDS. Salesforce states that the PDS acts as the external data definition and query source, and the integration does not create a corresponding Data 360 data object. Queries remain federated through the PDS connection.
For users configured in Tableau Cloud, Salesforce further states that record-level security and data access are inherited from the PDS. Consequently, a Tableau Next user can have access to the Tableau Next workspace, semantic model, dashboard, and visualization but still receive no usable data if their Tableau Cloud identity lacks access to the underlying Published Data Source.
Option B is incorrect because a PDS is a direct external connection and isn't represented as a DLO in Data
360. Option C is inconsistent with the scenario because the user already has the required Tableau Next asset permissions and access; moreover, the actual PDS authorization boundary remains relevant independently.
This question tests an important cross-platform rule: Tableau Next asset sharing does not override access controls enforced by the connected Tableau Cloud PDS.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Published Data Sources - > Tableau Cloud Trust - > PDS Authentication and Governance.
NEW QUESTION # 37
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)?
Answer: C
Explanation:
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.
NEW QUESTION # 38
A Tableau Next Consultant is working with a retail client's supply chain management team. The warehouse managers complain that they waste hours every week manually logging in and refreshing dashboards just to check if critical inventory levels have dropped below their safety stock limits. Which primary feature should the consultant enable to address this issue?
Answer: A
Explanation:
Inspector Proactive Data Alerts directly solve this requirement because they allow users to define conditions against Tableau Next metrics and receive notifications when those conditions become true. Salesforce describes the capability as allowing users to ask Tableau Agent to alert them when a metric exceeds a value, falls below a threshold, or moves outside an expected range.
For inventory management, the organization could define a metric representing available stock and configure an alert such as: "Alert me when inventory falls below the safety stock level." Inspector evaluates the configured condition and sends notifications through administrator-enabled delivery channels. This removes the need for warehouse managers to repeatedly open and refresh dashboards purely to determine whether intervention is required.
A Data Quality notification addresses issues with data reliability or processing rather than business KPI conditions. A dashboard subscription periodically delivers analytical content but is schedule-driven rather than specifically triggered by a critical inventory condition.
This is the defining distinction between passive dashboards and agentic analytics: Proactive Alerts monitor an explicitly defined metric condition and surface actionable information when attention is required.
References/Topics: Agentic Experiences - > Inspector - > Proactive Data Alerts - > Metric Conditions - > Threshold-Based Notifications.
NEW QUESTION # 39
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?
Answer: A
Explanation:
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.
NEW QUESTION # 40
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