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| Section | Weight | Objectives |
|---|---|---|
| Evaluate Current State | 22% | - Evaluate current data structures
|
| Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
| Design and Troubleshoot Calculations and Workbooks | 40% | - Implement and optimize calculations
|
| Plan and Prepare Data Connections | 22% | - Plan for data transformation
|
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NEW QUESTION # 41
Universal Containers is concerned about Personally Identifiable Information (PII) being exposed when using Data Connection and Analytics Creation capabilities to create calculated fields. How does Data Connection and Analytics Creation ensure data security when processing requests through the large language model (LLM)?
Answer: B
Explanation:
The correct security mechanism is the Einstein Trust Layer's PII masking capability. Salesforce guidance for AI-assisted calculated-field creation states that the agent may use information from the semantic model, including its schema and metadata, and that personally identifiable information is masked by the Einstein Trust Layer before information is sent to the LLM.
This allows Tableau Next's generative functionality to receive the semantic context required to construct useful calculated fields while applying Salesforce's enterprise AI security controls.
Option A describes a whole-model encryption/decryption workflow that is not the documented Tableau Next processing model. Option C is also incorrect because the system does not simply prohibit every field that could potentially contain PII. Instead, the Trust Layer applies masking and other controls to protect sensitive information while retaining useful analytical context.
More broadly, Salesforce states that Tableau Agent and Agentforce inherit the Einstein Trust Layer's security, governance, and trust mechanisms, including protections designed to prevent customer data from being retained by external LLMs for model training.
References/Topics: Agentic Experiences - > Einstein Trust Layer - > PII Masking - > Generative AI Calculated Fields.
NEW QUESTION # 42
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: A
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 # 43
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 # 44
A Tableau Next Consultant needs to migrate a newly developed Tableau Next semantic model and its associated dashboards from a sandbox to the production environment. The two environments are part of the same Salesforce organization hierarchy. Which deployment method should the consultant use to natively move these assets?
Answer: C
Explanation:
Among the listed choices, Inbound and Outbound Change Sets are the appropriate native Salesforce deployment mechanism. Tableau Next supports Salesforce Metadata API-based deployment capabilities, including change sets, for moving Tableau Next workspaces, visualizations, and dashboards between supported sandbox and target organizations. The workflow uses an outbound change set in the source org and an inbound change set in the target org.
The Data 360 Ingestion API is intended for bringing data into Data 360 rather than lifecycle deployment of Tableau Next metadata. The Tableau Cloud Site Migration Tool pertains to Tableau Cloud site migration and is not the Salesforce metadata deployment mechanism described here.
References/Topics: Managing Workspaces and Orgs - > Organize and Deploy Analytical Assets - > Deploy Tableau Next Assets.
NEW QUESTION # 45
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 # 46
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