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| Section | Weight | Objectives |
|---|---|---|
| Basic Setup & Admin | 10% | - Access Management
|
| Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Managing Workspaces & Orgs | 10% | - Personal Org
|
| Agentic Experiences | 25% | - Generative AI
|
| Data Setup | 20% | - Semantic Models
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Tableau Next Apps and Marketplace
|
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NEW QUESTION # 41
A Tableau Next Consultant is asked to configure proactive alerts for metrics. Which prerequisite must be met before alerts can be created?
Answer: B
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 # 42
Universal Containers connected an external Model Context Protocol (MCP)- compliant AI client to the Tableau Next MCP server so analysts can query their data from that client. During rollout, an analyst asks the Tableau Next Consultant how data access is determined when they query through the external client instead of signing in to Tableau Next directly. Which statement best describes how the integration governs what each analyst can access?
Answer: A
Explanation:
Tableau Next MCP sessions operate using the OAuth-authenticated identity of the individual user, so B is correct. The external MCP client does not become the authoritative security boundary. Instead, Tableau Next continues to enforce Salesforce/Tableau permissions and governance while exposing analytical capabilities to the external AI environment.
Salesforce's Tableau Next MCP documentation explicitly states that MCP sessions use OAuth-authenticated users and that permissions and restrictions associated with the user's profile apply to the MCP session.
Tableau user permissions, semantic-model definitions, and Trust Layer protections therefore remain effective when the user accesses Tableau Next through an external MCP-compatible client.
Salesforce's broader hosted-MCP architecture follows the same principle: tools enforce per-user authentication and standard Salesforce security controls rather than granting universal access based on connector configuration.
This design preserves governance across interoperability boundaries: Tableau Next remains the governed analytical source, while the external client functions as another interaction surface.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Integrate Analytics Across Platforms -
> Tableau Next MCP Server - > Permissions and Governance.
NEW QUESTION # 43
A Tableau Next Consultant identifies an issue with the default filtering on a metric embedded on an Opportunity record page. The metric is showing data aggregated for the parent account of the Opportunity, but it should be filtered to the Opportunity level. The consultant has verified that the relevant dimensions are added to the metric definition. Which change should the consultant make to the metric?
Answer: C
Explanation:
The existing parent-account filter is causing the metric to operate at Account scope, so merely adding another filter does not correctly express the desired record context. The consultant should remove that Account-based mapping and configure the metric dimension Opportunity ID to equal the Opportunity ID of the current Salesforce record. Therefore, B is correct.
Salesforce's embedded Tableau Next components support Record Value filtering on Lightning record pages.
With Record Value, the filter dynamically retrieves a field from the Salesforce record currently being viewed and compares it against the selected field or dimension in the Tableau Next data model.
Option A fails to remove the inappropriate parent Account filter, leaving conflicting or overly restrictive context. Option C maps unlike identifiers-Opportunity ID against Account ID-which is logically invalid.
This question tests contextual embedding architecture: the metric definition must expose the required dimension, while Lightning App Builder supplies the current record value used to dynamically scope the embedded metric.
References/Topics: Visualizations and Dashboards - > Embedded Tableau Next Metrics - > Record Value Filters - > Lightning Record Context.
NEW QUESTION # 44
An Agentforce Sales customer approaches a Tableau Next Consultant asking for analytics on pipeline health and rep performance. They have no existing analytics investment, want minimal setup time, and have no plans to build custom dashboards. What should the consultant recommend to meet this requirement?
Answer: A
Explanation:
The Tableau Next Sales Insights app is specifically designed to provide preconfigured sales analytics covering areas such as sales performance, pipeline health, and related operational sales measures. Salesforce describes Sales Insights as a packaged, data-driven sales solution that combines Tableau Next dashboards and analytics with Salesforce customer and sales data. It is intended to accelerate deployment rather than requiring organizations to design an analytics architecture from the ground up.
That positioning matches every constraint in the scenario: the organization already uses Agentforce Sales, has no existing analytics investment, wants minimal implementation effort, and does not intend to develop custom dashboards.
A custom Lightning Web Component would increase development, testing, governance, and maintenance effort. Full Tableau Next Creator licenses would give users broader authoring functionality than the stated requirement demands and would still leave the organization responsible for creating the analytical content itself.
Sales Insights also includes packaged semantic models, data objects, metrics, and dashboards, which substantially reduces the configuration burden compared with a greenfield Tableau Next implementation.
References/Topics: Basic Setup and Admin - > Tableau Next Sales Insights - > Packaged Analytics - > Agentforce Sales Integration.
NEW QUESTION # 45
A Tableau Next Consultant is asked to configure batch data transforms. Where is the output written?
Answer: A
Explanation:
Data Lake Object (DLO) is the correct answer among the available choices. A Data 360 Batch Data Transform requires an Output node where the transformed results are persisted. Salesforce documents that a Batch Data Transform can write its results to a new or existing target Data Lake Object, which can subsequently serve as input to identity resolution, calculated insights, segmentation, semantic modeling, or additional transforms.
A Data Stream Object is not the destination used to persist transformed output. Data streams provide ingestion pipelines that bring source data into Data 360. A Semantic Data Model is also not the physical output storage target; Tableau Semantics consumes governed Data 360 objects to provide measures, dimensions, relationships, calculations, and business context.
References/Topics: Data Setup - > Data 360 - > Batch Data Transforms - > Output Nodes - > Data Lake Objects.
NEW QUESTION # 46
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