Analytics-Con-202 Exam Dumps and Practice Questions

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.

Reference/Topics: Agentic Experiences -> Tableau Agent -> Analytics and Visualization Template -> Data Analysis Subagent -> Conversational Analytics.

When Acceleration for Data Connections is enabled, Tableau Next temporarily caches external-source data in Data 360 to reduce runtime latency and improve query performance. Two refresh approaches are available: Full Refresh and Incremental Refresh. Salesforce explicitly states that an incremental refresh processes records that have been added or changed since the previous refresh but does not update deleted data. Therefore, records removed from the source can remain present in the accelerated cache even after several incremental refresh cycles.

This explains why the 15-minute frequency does not solve the issue. Frequency controls how often incremental processing occurs; it does not change what the incremental algorithm processes. Option A incorrectly attributes the behavior to ordinary cache latency. Option C is also too broad because acceleration itself supports deletion synchronization when a Full Refresh is used. A full refresh removes the existing cached dataset and replaces it with the current source dataset.

Reference/Topics: Data Setup -> Data Connections -> Acceleration -> Full versus Incremental Refresh -> Cache Refresh Method.

A parameter is a user-controllable variable that can influence calculations, filtering, reference lines, and other interactive analytical behavior. Salesforce documents parameters as a mechanism for modeling scenarios and allowing dashboard users to provide inputs that affect analytical results. Parameters can be created for different data types and then referenced by calculated fields or dashboard controls.

For example, an organization could define a parameter named Target Discount and allow a user to enter 15%. A calculated field could then compare actual discount values with the parameter and dynamically change the resulting visualization. Tableau Next also supports parameter widgets that accept manually entered values or derive dynamic values from dashboard queries.

Option B incorrectly describes semantic-model relationship configuration. Relationships between DMOs are defined through relationship metadata and cardinality rather than parameters. Option C confuses parameters with governance and authorization controls.

The certification distinction is: parameters provide variable analytical input; calculated fields consume that input; semantic relationships define object connectivity; Data 360 governance controls security.

Reference/Topics: Data Setup -> Semantic Models -> Parameters -> Calculated Fields -> Interactive Scenario Modeling.

Tableau Next proactive alerts support delivery through Slack and Email, making option A the correct choice. The purpose of proactive alerting is to notify users when an important metric condition is met without requiring them to continually open dashboards and check the metric manually. Supporting collaboration and messaging channels allows the alert to reach executives in the tools they already monitor.

SMS-only delivery is not the supported model described here, and Microsoft Teams is not the exclusive proactive-alert channel. The alerting workflow is tied to Tableau Next metrics and the configured notification integrations. Once the metric, alert condition, and delivery configuration are in place, users can receive timely notification when the monitored value crosses the defined condition.

From an implementation perspective, consultants should confirm that the required alerting capability is enabled, that users can access the metric, and that the organization's Slack and email notification configuration is available. The analytical rule remains defined against the governed metric, while the selected channel determines how the resulting notification reaches the user.

Reference/Topics: Agentic Experiences -> Proactive Data Alerts -> Slack and Email Delivery.

The Tableau Next Analytics Embedding SDK supports an AnalyticsAgent component with both single-context and multi-component operating modes. In Multi-Component mode, the application omits the contextConfig property. The agent then automatically tracks the embedded AnalyticsDashboard and AnalyticsMetric components present on that page.

Therefore, B directly describes the intended architecture for a portal containing multiple analytical components.

Option A describes single-context mode, where contextConfig explicitly binds the agent to one dashboard, metric, or semantic model. That mode exists but is not a platform limitation. Option C introduces a same-semantic-model restriction that the current Embedding SDK documentation does not impose. The SDK explicitly describes automatic context tracking across embedded dashboard and metric components.

This capability is particularly important in embedded analytics because it allows conversational analytics to understand the broader analytical surface rather than forcing the external application to continually rebind the agent as users move between embedded components.

Reference/Topics: Embedding, Cross-Cloud, and Interoperability -> Tableau Next Embedding SDK -> AnalyticsAgent -> Multi-Component Mode.