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| Section | Weight | Objectives |
|---|---|---|
| Establish Governance and Support Published Content | 16% | - Apply governance strategy
|
| Design and Troubleshoot Calculations and Workbooks | 40% | - Optimize workbook performance
|
| Plan and Prepare Data Connections | 22% | - Plan for data transformation
|
| Evaluate Current State | 22% | - Map current state of analytics to future state
|
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NEW QUESTION # 64
A Tableau Next Consultant with a Creator- level Tableau Next permission set is working on an analytics project that requires combining a production data source with an external data source. The consultant wants to avoid setting up governance on the second data source. Which approach should the consultant take to work on this project and share the results with the stakeholder, who has a Self- Service Analyst- level permission set?
Answer: A
Explanation:
A Personal Org is specifically designed for self-service analysis where an analyst needs to combine governed corporate information with personal or specialized data without introducing that experimental dataset into the managed production governance environment.
Salesforce states that Personal Org enables analysts to combine production data with local or specialized datasets, create semantic models and visualizations, and iterate independently without affecting governed production content. Production assets can also be reused inside Personal Org while their existing production governance remains intact.
Once the analysis is ready, the Personal Org owner can share the relevant workspace with another eligible analyst and grant Viewer or Editor access. Salesforce explicitly documents sharing Personal Org workspaces with Tableau Next Analyst users.
Option B does not provide the isolated self-service environment intended for ungoverned analytical experimentation. Option C unnecessarily moves the analytical workflow into an external MCP client and does not represent the native Tableau Next solution for this use case.
References/Topics: Managing Workspaces and Orgs - > Personal Org - > Combine Governed and Personal Data - > Workspace Sharing.
NEW QUESTION # 65
A Tableau Next Consultant has developed a dashboard using a single semantic model. The dashboard includes filters, metrics, and visualizations. The consultant wants to be able to make changes to their filters and selections in their visualizations that update the metrics. They are already able to filter visualizations between each other by clicking into the views and using the filters on the page. What does the consultant need to validate to have the desired functionality?
Answer: C
Explanation:
For a Tableau Next Metric widget to respond dynamically to dashboard filtering, two conditions must be satisfied. First, the metric widget must be configured to receive dashboard filters. Second, the dimensions being used by those dashboard filters must exist in the metric definition as supported dimensions. Salesforce specifically documents that a Metric widget can only be filtered by dimensions explicitly included in the metric definition, typically as Additional Dimensions.
Therefore, C is the only complete answer.
Option A fails because enabling dashboard-filter reception is not enough if the metric definition lacks the necessary dimensions. Tableau Next cannot apply a filter against a dimensional grain the metric does not expose. Option B has the required dimensions but explicitly disables receipt of dashboard filters, preventing the widget from responding to dashboard interactions.
This distinction is important because visualizations and metric widgets have different interaction requirements. A dashboard visualization can participate naturally in interactive filtering, whereas a governed metric must expose the appropriate dimensions in its semantic definition before those dashboard selections can alter the displayed metric result.
References/Topics: Visualizations and Dashboards - > Metric Widgets - > Receive Dashboard Filters - > Metric Additional Dimensions.
NEW QUESTION # 66
Cloud Kicks (CK) uses CRM Analytics (CRMA) for operational embedded reporting within its Agentforce Sales console and Tableau Cloud for historical deep- dive executive dashboards. CK is now evaluating the addition of Tableau Next to its analytics architecture. Which statement accurately describes the distinct positioning benefit of adding Tableau Next to this multi- platform ecosystem?
Answer: A
Explanation:
Tableau Next's distinctive value is its agentic, conversational, and actionable analytics layer, so C is correct.
Salesforce positions Tableau Next as an end-to-end agentic analytics platform combining Tableau analytics, Agentforce AI, and the Data 360 semantic layer. It enables users to ask freeform analytical questions, receive grounded text and visual answers, obtain proactive insights, and initiate actions where work occurs.
The platform is therefore complementary to existing analytical investments. Tableau Cloud can continue supporting sophisticated traditional analytics and executive exploration, while CRM Analytics can continue supporting embedded Salesforce analytical use cases. Tableau Next introduces a governed semantic and agentic layer that makes data conversational and actionable.
Option A is too absolute. Salesforce interoperability capabilities explicitly support connecting Tableau Cloud to Tableau Semantics, illustrating coexistence rather than mandatory replacement. Option B also mischaracterizes Published Data Source integration: Tableau Next can build semantic models directly from supported Tableau Cloud PDS content; its purpose is not merely extraction into DMOs.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next Positioning - > Tableau Agent - > Tableau Cloud and CRM Analytics Coexistence.
NEW QUESTION # 67
A Tableau Next Consultant is asked to configure proactive alerts for metrics. Which prerequisite must be met before alerts can be created?
Answer: C
Explanation:
Inspector Proactive Data Alerts operate against Tableau Next metrics, and Tableau Next metrics are semantic definitions created within the Tableau Semantics layer. Salesforce describes a metric as a governed business KPI derived from measures in a semantic model. Metrics can then support exploration, goals, thresholds, and Inspector alerts.
Therefore, B represents the architectural prerequisite among the supplied options. An alert must have a valid metric whose governed definition provides the measure, aggregation, temporal context, and other analytical semantics Inspector evaluates.
A user does not need to embed the metric in a dashboard. Salesforce's documented alert workflow starts from the Tableau Next metric page, where the user opens Tableau Agent and asks it to create an alert against that metric.
Option A is also not the fundamental prerequisite described by architecture. Although alert-management experiences can be reached through metric-related surfaces, the key technical dependency is the existence of the metric itself.
Current configuration also requires Tableau Agent/Data Analysis and Inspector Proactive Alerts to be enabled administratively. These are environment-level prerequisites, but they are not among the answer choices.
References/Topics: Agentic Experiences - > Inspector Proactive Data Alerts - > Metrics - > Semantic Models - > Data Analysis.
NEW QUESTION # 68
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: B
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 # 69
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