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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and Prepare Data Connections | 22% | - Plan for data transformation
|
| Topic 2: Evaluate Current State | 22% | - Evaluate current data structures
|
| Topic 3: Design and Troubleshoot Calculations and Workbooks | 40% | - Optimize workbook performance
|
| Topic 4: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
>> Analytics-Con-202 Valid Test Objectives <<
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NEW QUESTION # 75
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: B
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 # 76
Cloud Kicks (CK) is designing a unified Customer 360 semantic data model (SDM). CK requires strict row level security (RLS) so that regional sales managers can only query and view accounts within their assigned territories. What is the recommended best practice for enforcing this security requirement in Tableau Next?
Answer: B
Explanation:
Tableau Next row-level security should be implemented through Data 360 governance policies, making B correct. Salesforce explicitly states that for Data 360 DLOs and DMOs, record-level security is defined through Data 360 policies. Tableau Next dashboards and visualizations built on those objects then inherit the enforced data visibility.
For example, the account dataset can include an attribute identifying the responsible territory or regional manager. A Data Governance access policy can evaluate the current user's relevant attributes and restrict returned records accordingly. Because security is enforced below the semantic and visualization layers, the same rules consistently protect dashboards, metrics, Tableau Agent queries, and other downstream consumers.
Option A describes the traditional Tableau USERNAME() pattern, but current Tableau Next guidance explicitly states that native user functions such as USERNAME() aren't the preferred/current RLS implementation for this architecture. Option C creates serious scalability, maintenance, and consistency problems and violates centralized governance principles.
The architectural rule is: define security as close to the governed data layer as possible so every downstream analytical experience inherits the same policy.
References/Topics: Data Setup - > Data 360 Governance - > Row-Level Security - > Access Policies - > Data Object Security.
NEW QUESTION # 77
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: B
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 # 78
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 # 79
Universal Containers (UC) is developing a new profitability dashboard within its Tableau Next Personal Org.
To ensure the dashboard is accurate, UC needs to incorporate the primary Sales data model object (DMO) which is governed and maintained in the production environment. The company's Tableau Next Consultant needs to bring this specific asset into its Personal Org workspace. Which action must the consultant take to use the production Sales DMO in UC's Personal Org?
Answer: C
Explanation:
Tableau Next Personal Org supports reusing governed production assets without requiring the analyst to recreate or independently govern those assets. The documented procedure is to open or create the Personal Org workspace, select Add - > Data, choose Production from the Org dropdown, identify the production workspace and asset type, and then add the required asset to the workspace.
Therefore, A precisely matches the official workflow.
The reused production asset remains governed by production controls. Salesforce notes that assets reused from production are effectively read-only references; if the source asset is removed or the user loses access, the corresponding reused asset in the Personal Org stops functioning.
Cloning the DMO is unnecessary and would contradict the intended reuse model. A Data Kit is a deployment mechanism used to move supported assets between managed environments; it is not the Personal Org workflow for referencing an individual production DMO.
This design supports self-service analytics while maintaining centralized governance: analysts can combine authoritative enterprise data with their own analytical work without creating unmanaged duplicate definitions.
References/Topics: Managing Workspaces and Orgs - > Personal Org - > Reuse Production Org Assets - > Add Data.
NEW QUESTION # 80
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