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| Section | Weight | Objectives |
|---|---|---|
| Data Setup | 20% | - Data 360
|
| Basic Setup & Admin | 10% | - Tableau Next Setup
|
| Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Managing Workspaces & Orgs | 10% | - Workspace and Asset Management
|
| Agentic Experiences | 25% | - Generative AI
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Developer Tools and APIs
|
>> Salesforce Analytics-Con-202 Prepaway Dumps <<
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NEW QUESTION # 50
Cloud Kicks stores historical customer transaction data inside Snowflake. The marketing team wants to track campaign performance in Tableau Next. However, IT compliance strictly prohibits creating duplicate copies of this dataset due to storage costs and security synchronization risks. Which architectural feature should a Tableau Next Consultant implement to meet these requirements?
Answer: C
Explanation:
Zero Copy Data Federation is the correct architectural approach because it allows Tableau Next and Data 360 to work with Snowflake-hosted data without creating a separate duplicated dataset. The Snowflake tables remain in the source platform while the federation layer exposes them for governed analytical use. This directly addresses both storage-cost concerns and the compliance risk associated with maintaining synchronized copies.
Option B would move the data into Salesforce custom objects through an integration flow, which creates another persisted copy and increases synchronization and governance overhead. Option C also creates replicated data through scheduled batch processing and therefore violates the stated requirement to avoid duplication. Zero-copy access keeps the source system authoritative while making the dataset available for semantic modeling and Tableau Next analysis.
The important architecture pattern is federation rather than extraction: connect Snowflake, expose the required federated data objects, apply the necessary governance, and then build the Tableau Next semantic layer over those objects. This minimizes data movement while preserving analytical accessibility.
References/Topics: Data Setup - > Data 360 - > Snowflake - > Zero Copy Data Federation - > Tableau Next.
NEW QUESTION # 51
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: B
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 # 52
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 # 53
A Tableau Next Consultant is demonstrating how Tableau Agent provides transparency for its responses. A user asks Tableau Agent a question and receives a response with a visualization. The user now needs to examine how the agent arrived at the response. Which action must the user take in Tableau Agent to see this information?
Answer: A
Explanation:
The user should select Sources. Tableau Agent's conversational analytics experience exposes a Sources control specifically to provide transparency into how a response was generated.
Salesforce documents that after Tableau Agent returns an analytical answer, users can select Sources to review details about how the agent analyzed the request, including the data sources and metrics used during the analysis. Salesforce also states that conversational analytics responses include information about the sources and reasoning used in the analysis, supporting traceability and user trust.
Option B concerns visualization configuration rather than agent reasoning and source grounding. Option C is not the documented Tableau Agent workflow for exposing reasoning provenance to end users.
This capability is important because Tableau Agent performs semantic queries against the underlying semantic model, synthesizes the returned data into a textual response, and generates a suitable visualization.
Sources helps users understand the analytical basis of that output rather than requiring them to accept an opaque AI-generated answer.
References/Topics: Agentic Experiences - > Tableau Agent - > Conversational Analytics - > Sources - > Response Transparency and Grounding.
NEW QUESTION # 54
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 # 55
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