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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Workspaces & Orgs | 10% | - Personal Org
|
| Topic 2: Embedding, Cross-Cloud, & Interoperability | 20% | - Developer Tools and APIs
|
| Topic 3: Basic Setup & Admin | 10% | - Agentic Analytics
|
| Topic 4: Agentic Experiences | 25% | - Agentic Readiness
|
| Topic 5: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 6: Data Setup | 20% | - Data Architecture
|
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NEW QUESTION # 41
A Tableau Next Consultant is asked to configure proactive alerts for metrics. Which prerequisite must be met before alerts can be created?
Answer: B
Explanation:
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.
References/Topics: Agentic Experiences - > Inspector Proactive Data Alerts - > Metrics - > Semantic Models - > Data Analysis.
NEW QUESTION # 42
A Tableau Next Consultant receives a request from a business user to help troubleshoot an issue with setting data alerts using Tableau Agent. The consultant verifies that the business user has been granted a Tableau Next Consumer license and is accessing Tableau Agent from a page with Tableau Next on it. Which reason explains why the business user is unable to set the alert?
Answer: B
Explanation:
The limiting factor in this scenario is the user's license level. Setting proactive data alerts through Tableau Agent is an authoring-oriented capability that requires Tableau Next Creator access. A Consumer user can view and interact with published analytical content, but the Consumer persona does not receive the full set of creation and configuration capabilities needed to define alert conditions.
The fallback semantic model in option B is used when Tableau Agent lacks clear analytical context, such as when a user invokes the agent from a surface that does not already identify the relevant Tableau Next asset.
Here, the user is explicitly accessing Tableau Agent from a page that contains Tableau Next content, so lack of fallback scoping is not the primary issue. Option A can prevent analysis of a particular metric, but the question establishes a broader inability to set the alert and emphasizes that the user has a Consumer license.
The appropriate remediation is therefore to use the Creator-level entitlement for users who must configure proactive alerts. This preserves the distinction between consumption permissions and analytical creation
/configuration permissions.
References/Topics: Agentic Experiences - > Tableau Agent - > Proactive Data Alerts - > Creator Permissions.
NEW QUESTION # 43
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 # 44
A Tableau Next Consultant is advising a retail company that wants to track its monthly sales conversion rate against a fixed benchmark in Tableau Next. The metric is non- cumulative. Which feature should the consultant recommend configuring on the metric?
Answer: C
Explanation:
A threshold is the appropriate configuration because the requirement is to evaluate a non-cumulative metric against a fixed benchmark. Salesforce describes thresholds as contextual reference values that classify a metric into meaningful ranges such as healthy, at risk, or critical. Salesforce specifically notes that thresholds can be used independently of goals and are particularly useful for non-cumulative metrics.
A goal is better suited to measuring progress toward a target, particularly when progress accumulates over a defined time period. For example, a sales-revenue goal can indicate whether the organization is on pace to reach a quarterly target. By contrast, a conversion percentage is normally evaluated as a current rate rather than accumulated toward a total.
Forecasting Insights addresses a different analytical requirement: estimating likely future metric behavior from historical patterns. It does not establish the fixed performance benchmark required here.
For exam purposes, distinguish goal = progress toward target, threshold = contextual/static performance boundary, and forecast = projected future behavior.
References/Topics: Visualizations and Dashboards - > Metrics - > Goals and Thresholds - > Non- Cumulative Metrics.
NEW QUESTION # 45
During a discovery call, a client tells their Tableau Next Consultant that they love the idea of embedding the Analytics and Visualization Agent into an external portal, but that they have three dashboards on the same page: Sales Performance, Risk Exposure, and Regional Revenue. What is true about the agent's capabilities in this scenario?
Answer: B
Explanation:
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.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next Embedding SDK - > AnalyticsAgent - > Multi-Component Mode.
NEW QUESTION # 46
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