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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and Prepare Data Connections | 22% | - Design row-level security (RLS) and advanced connections
|
| Topic 2: Design and Troubleshoot Calculations and Workbooks | 40% | - Optimize workbook performance
|
| Topic 3: Establish Governance and Support Published Content | 16% | - Deploy and manage content lifecycle
|
| Topic 4: Evaluate Current State | 22% | - Evaluate current data structures
|
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NEW QUESTION # 72
Cloud Kicks (CK) is designing a unified Customer 360 semantic data model (SDM). CK requires strict row level security (RLS) so that regional sales managers can only query and view accounts within their assigned territories. What is the recommended best practice for enforcing this security requirement in Tableau Next?
Answer: C
Explanation:
Tableau Next row-level security should be implemented through Data 360 governance policies, making B correct. Salesforce explicitly states that for Data 360 DLOs and DMOs, record-level security is defined through Data 360 policies. Tableau Next dashboards and visualizations built on those objects then inherit the enforced data visibility.
For example, the account dataset can include an attribute identifying the responsible territory or regional manager. A Data Governance access policy can evaluate the current user's relevant attributes and restrict returned records accordingly. Because security is enforced below the semantic and visualization layers, the same rules consistently protect dashboards, metrics, Tableau Agent queries, and other downstream consumers.
Option A describes the traditional Tableau USERNAME() pattern, but current Tableau Next guidance explicitly states that native user functions such as USERNAME() aren't the preferred/current RLS implementation for this architecture. Option C creates serious scalability, maintenance, and consistency problems and violates centralized governance principles.
The architectural rule is: define security as close to the governed data layer as possible so every downstream analytical experience inherits the same policy.
References/Topics: Data Setup - > Data 360 Governance - > Row-Level Security - > Access Policies - > Data Object Security.
NEW QUESTION # 73
Universal Containers wants to ensure its users are able to view, explore, and follow metrics. What is the minimum license level that grants this ability?
Answer: C
Explanation:
The minimum general Tableau Next persona that can view, explore, and follow metrics is the Tableau Unmetered Consumer level represented by B.
Salesforce documents that Consumer users can access the Tableau Next home experience, use Tableau Agent, view workspaces, dashboards, and visualizations, and view, explore, and follow metrics. Salesforce's metric- specific documentation likewise identifies Tableau Unmetered Consumer and equivalent Consumer/Self- Service Consumer permission sets as sufficient to follow metrics.
A Self-Service Analyst can also perform these actions, but that persona includes significantly broader capabilities and therefore is not the minimum required level. Analysts can work with Tableau Next Studio and, depending on the permission set, create or edit analytical content.
An Included App license is narrower. Included App/Limited Consumer personas are designed primarily to interact with prepackaged Tableau Next Apps for Salesforce rather than provide general Tableau Next home and metric exploration capabilities.
The exam principle is to select the least-privileged persona that fully satisfies the requirement, rather than assigning authoring permissions merely because they also include consumption capabilities.
References/Topics: Basic Setup and Admin - > Tableau Next Licenses and Permission Sets - > Consumer - > Metric Exploration and Following.
NEW QUESTION # 74
A Tableau Next Consultant is asked to configure proactive alerts for metrics. Which prerequisite must be met before alerts can be created?
Answer: C
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 # 75
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: B
NEW QUESTION # 76
A mature Tableau customer wants to use the Analytics Agent in Tableau Next. However, they have heavily invested in complex data models and calculations within Tableau Cloud Published Data Sources (PDSs) and refuse to rebuild this logic from scratch in Data 360. How should a Tableau Next Consultant meet this requirement using native interoperability?
Answer: C
Explanation:
Tableau Next provides native interoperability with Tableau Cloud Published Data Sources (PDSs). Salesforce allows a consultant to create a Data 360 semantic model by connecting directly to an existing PDS after establishing the required trust between the Salesforce org and Tableau Cloud site.
The PDS remains the external data definition and query source. Tableau Next inherits fields such as their names and data types, and the semantic model can then be enriched with calculated fields, metrics, descriptions, and business context. Critically, Salesforce states that this approach allows organizations to use existing Tableau content without data or metadata migration.
That precisely addresses the customer's concern about preserving substantial prior investment in Tableau Cloud modeling and calculations.
Options A and B introduce mechanisms that are not the native Tableau Next interoperability pattern documented for PDS reuse. There is no requirement to translate the PDS into Snowflake SQL or convert it into a CRM Analytics recipe.
This architecture enables Tableau Next to extend rather than replace mature Tableau investments, while exposing existing governed analytical logic to Tableau Agent.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Cloud PDS - > Tableau Semantics - > Create Semantic Model from Published Data Source.
NEW QUESTION # 77
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