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| Section | Weight | Objectives |
|---|---|---|
| Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Data Setup | 20% | - Data 360
|
| Managing Workspaces & Orgs | 10% | - Asset Deployment
|
| Basic Setup & Admin | 10% | - Tableau Next Activation and Management
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Developer Tools and APIs
|
| Agentic Experiences | 25% | - Agentic Readiness
|
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NEW QUESTION # 65
Sales managers at Universal Containers (UC) want to receive proactive data alerts for their Tableau Next metrics directly within their Slack channels. UC has already enabled the Proactive Alert setting and Agentforce for Analytics in the Salesforce org. Which additional prerequisite must be met for users to receive these alerts in Slack?
Answer: C
Explanation:
The additional requirement is to enable Slack for Tableau Next, represented by option B's Collaboration with Slack and Tableau Next setting. Salesforce's current administrative workflow requires Salesforce and Slack to be connected and the Collaborate with Slack and Tableau Next option enabled in Tableau Next Administration.
For proactive alerts specifically, Salesforce states that an administrator must enable Slack for Tableau Next and configure Agentforce in Slack. Once properly configured, Inspector Proactive Data Alerts can deliver notifications into Tableau Next and Slack when a user-defined metric condition is satisfied.
Assigning a generic "Slack User" permission set is not the documented Tableau Next prerequisite in this scenario. Users instead require an eligible Tableau Next permission set and access to the relevant asset.
Likewise, a separate analytics agent created specifically for Slack is not required merely to receive Inspector alerts.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Slack Integration - > Inspector Proactive Data Alerts - > Collaborate with Slack and Tableau Next.
NEW QUESTION # 66
A Tableau Next Consultant is asked to configure workspace deployment. What is the correct sequence?
Answer: A
Explanation:
The correct deployment sequence in this question is **Semantic Models # Workspace # Dashboards # Visualizations**. The semantic model is deployed first because it provides the governed analytical definitions and data context required by downstream Tableau Next assets. The workspace follows because it establishes the organizational container in which the analytical content is managed and shared.
Dashboards are then deployed as the higher-level presentation assets, followed by the associated visualizations according to the sequence specified in the supplied deployment model. Maintaining the prescribed order prevents deployment from starting with downstream assets before their required semantic and organizational foundations are available.
Options B and C begin with assets that depend on components appearing later in their sequences, so they do not follow the required dependency progression. For certification purposes, memorize the sequence exactly as presented: **Semantic Models first, then Workspace, then Dashboards, then Visualizations**. Deployment planning should also include the relevant access and governance configuration so users can open the migrated assets successfully after publication.
References/Topics: Managing Workspaces and Orgs - > Workspace Deployment - > Publishing Sequence.
NEW QUESTION # 67
A Tableau Next Consultant is adding a field description to a new calculated field. What are characteristics of a good field description?
Answer: C
Explanation:
A high-quality semantic field description should be clear, concise, and business-contextual, making C correct.
Tableau Agent relies heavily on semantic metadata to determine what fields represent, when they should be used, and how they relate to a user's natural-language question.
Salesforce's Tableau Next semantic-model guidance explicitly recommends descriptions that are clear and concise and add context. Effective descriptions explain the field's purpose, business scope, meaning, relevant data behavior, and relationships when appropriate. Salesforce also recommends avoiding vague definitions or descriptions that simply repeat the field label.
Option A overemphasizes length and technical calculation detail. Excessively long descriptions can introduce unnecessary complexity and ambiguity; the objective is useful business meaning, not exhaustive implementation documentation. Option B captures brevity but omits the most important factor: business context.
For example, a field named Net_Revenue is better described as "Revenue from finalized product sales after discounts, returns, and allowances" than merely "Calculated net revenue." The former gives the agent information needed to map business-language requests accurately.
References/Topics: Data Setup - > Semantic Model AI Readiness - > Field Metadata - > Business-Friendly Descriptions.
NEW QUESTION # 68
Cloud Kicks (CK) has created a semantic model and associated metrics to help improve the conversion rate for marketing driven leads. Sales and marketing teams both regularly review these metrics. When reviewing the project's results, CK discovers that both teams are looking at the same metrics, but often have parallel discussions in Slack without awareness of the other's conversation. Which functionality of Tableau Next in Slack should a Tableau Next Consultant highlight to improve cross- team collaboration?
Answer: C
Explanation:
The Conversations tab is specifically designed to expose related Slack discussions associated with Tableau Next analytical assets. When a Tableau Next metric, dashboard, or visualization is shared in Slack, users can open its preview and select Conversations to browse related Slack channels, threads, or direct-message discussions that they are authorized to access.
This directly addresses the scenario: sales and marketing are already discussing the same analytical content, but those discussions are fragmented. Surfacing related conversations in the asset context enables teams to discover existing dialogue instead of unknowingly creating duplicate parallel threads.
Option A changes how metrics are surfaced but does not address visibility into conversations already occurring around those metrics. Option C creates yet another broadcast mechanism and does not consolidate or expose existing contextual discussions.
A key design point is that Slack permissions continue to apply. The Conversations tab does not reveal conversations that the current user is unauthorized to access. Instead, it creates analytical context across conversations the user can already legitimately view, preserving Slack security while improving cross-team awareness.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next in Slack - > Related Conversations - > Conversations Tab.
NEW QUESTION # 69
A Tableau Next Consultant successfully creates a calculated field using Data Connection and Analytics Creation Subagent involving a Sales data model object (DMO) and Customer DMO. However, when attempting a similar calculation involving a Territory DMO, Data Pro fails. What is the most likely cause?
Answer: B
Explanation:
Data Pro-now increasingly referred to under Semantic Modeling functionality-depends on relationships already defined between the data model objects participating in a generated calculated field. Salesforce explicitly states that a predefined relationship must exist between DMOs for Data Pro to create a calculated field spanning those objects. The agent cannot construct a valid cross-object calculation when the required relationship does not exist.
Therefore, if calculations involving Sales and Customer work but introducing Territory causes the operation to fail, the most likely issue is that Territory is not relationally connected to the relevant objects.
Good metadata and field descriptions improve semantic clarity and AI accuracy, but insufficient descriptions are not the hard technical prerequisite being tested. Likewise, Salesforce does not document a general two- DMO maximum corresponding to option C.
The broader architectural principle is that generative semantic-model functionality does not replace proper data modeling. AI features consume the semantic model's existing object structure, metadata, and relationships. Consultants must establish valid relationships before expecting Tableau Agent to synthesize calculations involving fields from multiple objects.
References/Topics: Data Setup - > Semantic Models - > DMO Relationships - > AI-Generated Calculated Fields - > Data Pro/Semantic Modeling.
NEW QUESTION # 70
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