DumpsKing has one of the most comprehensive and top-notch Salesforce Analytics-Con-202 Exam Questions. We eliminated the filler and simplified the Salesforce Certified Tableau Next Consultant preparation process so you can ace the Salesforce certification exam on your first try. Our Salesforce Analytics-Con-202 Questions include real-world examples to help you learn the fundamentals of the subject not only for the Salesforce exam but also for your future job.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Experiences | 25% | - Agentic Readiness
|
| Topic 2: Visualizations & Dashboards | 15% | - Dashboard Actions
|
| Topic 3: Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Topic 4: Data Setup | 20% | - Semantic Models
|
| Topic 5: Managing Workspaces & Orgs | 10% | - Asset Deployment
|
| Topic 6: Basic Setup & Admin | 10% | - Access Management
|
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NEW QUESTION # 29
A Tableau Next Consultant wants to implement a Tableau Agent within Tableau Next to provide conversational insights and automated chart summaries for its sales operations managers. The deployment strategy requires using a preconfigured platform framework to accelerate the setup. How should the consultant deploy this specific conversational analytics capability?
Answer: C
Explanation:
Tableau Agent conversational analytics is delivered through the Analytics and Visualization agent template.
Salesforce's current documentation states that enabling Tableau Agent makes the Analytics and Visualization template and its prebuilt capabilities available in Agentforce Builder or Agentforce Studio. The consultant then creates an agent from that template and includes the Data Analysis subagent to enable conversational analytical responses and generated visualizations.
Therefore, B is correct.
Einstein Discovery Prediction is associated with predictive analytics and is not the preconfigured framework for Tableau Agent's conversational analytics. Tableau Next Marketplace contains reusable analytical assets and templates but is not the mechanism for constructing the core Analytics and Visualization agent.
References/Topics: Agentic Experiences - > Tableau Agent - > Analytics and Visualization Template - > Data Analysis Subagent - > Conversational Analytics.
NEW QUESTION # 30
Cloud Kicks has built a highly curated semantic data model (SDM) in Tableau Next containing all of its business rules, metric definitions, and generative AI field descriptions. However, another team of analysts want to use this exact same business logic to build custom visualizations in Tableau Cloud. What should the Tableau Next Consultant recommend to this team?
Answer: B
Explanation:
The correct approach is to use the Tableau Semantics connector from Tableau Cloud. Salesforce explicitly supports connecting Tableau Desktop and Tableau Cloud directly to semantic models defined in Data 360
/Tableau Semantics. This allows downstream Tableau authors to consume the same governed measures, dimensions, calculations, relationships, metrics, and business definitions that Tableau Next uses.
Option B would undermine the principal benefit of the semantic layer. Reconnecting directly to Data 360 objects and recreating joins would force analysts to reconstruct business logic and could lead to inconsistent calculations or relationships across analytical applications. Option C is also incorrect because Data Kits are Salesforce/Data 360 deployment mechanisms; they are not used to install semantic models into Tableau Cloud as independent Tableau data models.
This interoperability capability is strategically important: Tableau Semantics provides one governed analytical definition layer that can be consumed across Tableau Next, Tableau Cloud, Tableau Desktop, AI experiences, and other supported Salesforce applications. Analysts can therefore create custom Tableau Cloud visualizations without duplicating semantic logic.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Semantics Connector - > Tableau Cloud - > Reusable Semantic Models.
NEW QUESTION # 31
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: A
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 # 32
The client wants AI-generated insights about Gross Margin % in Tableau Next. Where is the best place to define this context for the agent?
Answer: C
NEW QUESTION # 33
A Tableau Next Consultant is asked to configure batch data transforms. Where is the output written?
Answer: B
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 # 34
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