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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate Current State | 22% | - Evaluate current data structures
|
| Topic 2: Design and Troubleshoot Calculations and Workbooks | 40% | - Implement and optimize calculations
|
| Topic 3: Establish Governance and Support Published Content | 16% | - Apply governance strategy
|
| Topic 4: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
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NEW QUESTION # 34
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: B
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 # 35
A Tableau Next Consultant at Ursa Major Solar is creating a Tableau Next dashboard to monitor at- risk opportunities. The sales manager needs a way for sales reps to initiate follow- up tasks for specific opportunities directly from a dashboard widget. This action must trigger an existing Salesforce flow designed for task creation. Which action type should the consultant add to the widget to meet this requirement?
Answer: A
Explanation:
The consultant should configure a Salesforce Flow action on the dashboard widget. Tableau Next supports single-click dashboard actions that can directly initiate Salesforce Flows using dashboard context. Salesforce even documents a virtually identical use case: enabling sales representatives to create a follow-up task directly from a dashboard widget displaying at-risk opportunities.
When configuring the action, the consultant selects Salesforce Flow as the action type, chooses an eligible flow, and maps the flow's input variables to static or dynamic dashboard values. Tableau Next currently supports standard screen flows and autolaunched flows for this capability.
A parameter action is fundamentally an analytical interaction mechanism and is not the native mechanism for executing a Salesforce Flow. Option C refers generally to Salesforce actions on visualization records, but the scenario explicitly identifies a dashboard widget that must initiate an existing Salesforce Flow. The dedicated Flow action is therefore the precise implementation.
This capability illustrates Tableau Next's actionable-analytics design: the user moves directly from recognizing an analytical condition to initiating an operational Salesforce process without leaving the dashboard.
References/Topics: Visualizations and Dashboards - > Single-Click Actions - > Salesforce Flow Actions - > Dynamic Input Mapping.
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
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: A
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 # 38
Cloud Kicks (CK) wants to scale out its semantic data tier but has limited Tableau Next Consultant resources.
CK wants to identify tasks where Tableau Agent's generative AI capabilities can directly accelerate the delivery of Tableau Next components. Which task can Tableau Agent directly perform within the semantic data model configuration canvas?
Answer: C
Explanation:
Generative AI capabilities in Tableau Semantics can directly accelerate semantic-model authoring by drafting calculated-field formulas and semantic descriptions, making A the correct choice. Salesforce supports Draft with Einstein, where the consultant describes the calculation in natural language and Einstein generates the calculated-field definition, including formula-related configuration that the author can review and save.
Salesforce also provides AI-generated semantic descriptions. These descriptions supply business context to downstream conversational analytics and help Tableau Agent correctly discover and interpret objects and fields.
Identity resolution and DMO mapping belong to Data 360 data-modeling and identity-management workflows, not the semantic-model generative-authoring capability described in this scenario. Dashboard and visualization construction is also a separate analytics-authoring process rather than the specified operation within the semantic data model configuration canvas.
The exam principle is that Tableau Agent/Semantics AI reduces semantic-authoring friction by translating natural-language analytical intent into semantic definitions, relationships, descriptions, and calculations.
References/Topics: Agentic Experiences - > generative AI for Tableau Next components; Create Semantic Models - > AI-assisted calculated fields and descriptions.
NEW QUESTION # 39
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