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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Setup | 20% | - Semantic Models
|
| Topic 2: Basic Setup & Admin | 10% | - Tableau Next Activation and Management
|
| Topic 3: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 4: Embedding, Cross-Cloud, & Interoperability | 20% | - Developer Tools and APIs
|
| Topic 5: Agentic Experiences | 25% | - Generative AI
|
| Topic 6: Managing Workspaces & Orgs | 10% | - Asset Deployment
|
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NEW QUESTION # 70
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: B
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 # 71
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 # 72
A Tableau Next Consultant shares a metric in Slack that contains sensitive data governed by Data 360 policies. A user who does not have a Tableau Next license views the shared message in Slack and can see the metric image. The consultant is concerned there's a problem with the data security setup. Which statement correctly describes this behavior?
Answer: B
Explanation:
For a standard Tableau Next Slack message preview, Salesforce explicitly warns that anyone who can see the Slack message can see its preview, including filters applied when it was shared. Consequently, users should verify that the preview does not expose inappropriate sensitive information before posting it into a channel or conversation.
Tableau Next's Slack integration generates previews according to the sender's permission access and Tableau Next governance context. Once that snapshot-style preview is shared, visibility of the message governs who can see that static representation.
Therefore, C correctly describes the behavior. There is no indication that Data 360 security has malfunctioned.
Option A is wrong because recipients do not automatically obtain unrestricted interactive access to the underlying Tableau Next asset. Option B incorrectly assumes that each static Slack preview is dynamically authorization-checked as though the recipient were opening Tableau Next.
A critical distinction is that Slack Canvas cards behave differently: Canvas previews can refresh using each viewer's Tableau Next access, and unauthorized users cannot see the live underlying content.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next in Slack - > Shared Previews - > Governance.
NEW QUESTION # 73
A Tableau Next Consultant interacts with a Tableau Next dashboard and identifies an issue with a Tableau Next metric: It's using the wrong date field to trend the measure field. The consultant has confirmed that the correct date field exists within the semantic model. How should the consultant update the Tableau Next metric?
Answer: A
Explanation:
The consultant should modify the metric definition in the underlying semantic model. Tableau Next metrics are semantic-layer objects. Salesforce defines a metric as a business KPI derived from one or more measures together with a date field, and metrics are built from semantic models.
Therefore, selecting which date dimension governs the metric's time trend is part of the metric's semantic definition rather than dashboard presentation logic. The dashboard consumes the governed metric; changing the dashboard would not correct the authoritative metric definition.
Option B is also inappropriate. Data Lake Objects and Data Model Objects define the underlying physical
/logical data available in Data 360. Because the correct date field already exists in the semantic model, there is no requirement to modify the underlying DLO or DMO. The problem lies specifically in which existing semantic field the metric uses for its temporal definition.
This reflects a central Tableau Next architectural principle: reusable analytical business logic-including metrics, calculations, temporal semantics, and contextual definitions-should reside in Tableau Semantics, while dashboards remain downstream consumption and presentation layers.
References/Topics: Data Setup - > Semantic Models - > Metrics - > Measure and Date Field Configuration.
NEW QUESTION # 74
A Tableau Next Consultant is asked to configure row-level security (RLS) for sensitive HR data. Where should this be defined?
Answer: C
Explanation:
Row-level security for this scenario should be defined in the semantic model so the governed analytical layer determines which records each user is allowed to retrieve. By applying RLS at the semantic-model level, the access rule travels with the analytical definitions used by metrics, visualizations, dashboards, and agentic experiences instead of depending on a particular dashboard layout.
Dashboard filters and visualization properties are presentation controls. They can alter what appears on a given screen, but they are not an appropriate substitute for a security rule because a user could potentially access the same underlying semantic content through another approved analytical surface. The restriction therefore belongs in the model that governs how analytical queries resolve against the sensitive HR dataset.
For an HR scenario, the RLS rule can be based on attributes such as employee, manager, department, region, or another authorized access dimension. The objective is to ensure that every downstream analytical experience receives only the rows permitted for the signed-in user. This centralizes security behavior and reduces the risk of inconsistent filtering across dashboards.
References/Topics: Data Setup - > Semantic Models - > Row-Level Security - > Governed HR Data.
NEW QUESTION # 75
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