Salesforce Analytics-Con-202 Exam Dumps

The additional requirement is to enable Slack for Tableau Next, represented by option B's Collaboration with Slack and Tableau Next setting. Salesforce's current administrative workflow requires Salesforce and Slack to be connected and the Collaborate with Slack and Tableau Next option enabled in Tableau Next Administration.

For proactive alerts specifically, Salesforce states that an administrator must enable Slack for Tableau Next and configure Agentforce in Slack. Once properly configured, Inspector Proactive Data Alerts can deliver notifications into Tableau Next and Slack when a user-defined metric condition is satisfied.

Assigning a generic ''Slack User'' permission set is not the documented Tableau Next prerequisite in this scenario. Users instead require an eligible Tableau Next permission set and access to the relevant asset. Likewise, a separate analytics agent created specifically for Slack is not required merely to receive Inspector alerts.

Reference/Topics: Embedding, Cross-Cloud, and Interoperability -> Slack Integration -> Inspector Proactive Data Alerts -> Collaborate with Slack and Tableau Next.

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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.

Reference/Topics: Agentic Experiences -> Inspector Proactive Data Alerts -> Metrics -> Semantic Models -> Data Analysis.

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To enable Analytics Agent effectively in a multi-org environment, two critical foundational elements are required:

1. Semantic Model & Data Quality: The Analytics Agent relies on well-defined semantic models and high-quality integrated data to generate accurate insights. Without proper semantic design (tables, columns, metrics, relationships) and reliable data integration through Data 360, the agent cannot answer user questions reliably.

2. Agentic Readiness & Governance: Before enabling the agent, organizations must assess whether the user population is ready for AI-driven analytics, establish appropriate access controls, and define AI governance policies (what data the agent can access, compliance requirements, etc.).

Option 1 correctly identifies both semantic/integration quality AND access/LLM governance. Option 5 correctly identifies data completeness (Data 360 connectors and semantic layer) AND Agentic readiness assessment. Both are comprehensive and reflect the exam's emphasis on data setup and agentic readiness objectives.

Options 2, 3, and 4 focus on secondary concerns (licensing, hardware, naming conventions, performance) that do not directly address agentic enablement or data architecture readiness. Dashboard templates and bandwidth are operational details, not foundational architecture requirements.

The correct answer is to build a unified semantic model in Data 360 that connects to all three sources, defines relationships at the semantic layer, and virtualizes the data without copying it. This leverages core Data 360 capabilities—specifically data integration and semantic modeling—which are designed to federate data across multiple cloud sources. The semantic layer abstracts complexity and enables users to explore data via a single, well-defined model without redundant copying.

The first option fails because it leaves join complexity to end users, defeating the purpose of semantic modeling. The third option is impractical for cloud-scale data and doesn't leverage Data 360. The fourth option places join logic at the dashboard layer rather than the semantic layer, reducing reusability. The fifth option violates the principle of avoiding unnecessary data duplication and increases latency through staging—Data 360 supports virtualization without copying.

Tableau Next asset sharing and Data 360 data access are separate layers. Granting users Viewer access to a workspace can provide inherited access to its dashboards, visualizations, and semantic models, but it does not automatically grant access to the underlying Data 360 data. Salesforce explicitly states that access to a semantic model requires access to the data space containing the model as well as access to the objects and fields used by that model.

Therefore, C is correct.

End users do not require Edit access to underlying Data 360 objects merely to view dashboard data, making A unnecessarily privileged. Similarly, B is incorrect because Consumer-level permission sets can view Tableau Next assets; Platform Analyst or Self-Service Analyst permissions are not universally required for dashboard consumption.

The architecture can be understood as three layers: license/permission access, Tableau Next asset sharing, and Data 360 data visibility/governance. Successful dashboard rollout requires all applicable layers to authorize the user. Sharing alone cannot override an administrator's data-space, DMO, DLO, field, or policy restrictions.

Reference/Topics: Managing Workspaces and Orgs -> Asset Sharing -> Data Spaces -> Data 360 Governance -> Semantic Model Access.

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