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| Section | Weight | Objectives |
|---|---|---|
| Basic Setup & Admin | 10% | - Tableau Next Activation and Management
|
| Embedding, Cross-Cloud, & Interoperability | 20% | - Cross-Platform Workflows
|
| Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Data Setup | 20% | - Data Architecture
|
| Agentic Experiences | 25% | - Analytics Agent
|
| Managing Workspaces & Orgs | 10% | - Personal Org
|
>> Latest Analytics-Con-202 Exam Questions Vce <<
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NEW QUESTION # 33
What is a logical view within a semantic data model?
Answer: B
Explanation:
A Logical View is a semantic data object that combines underlying objects through explicitly configured joins or unions and presents the resulting structure as a single analytical object. Of the choices provided, A correctly captures this concept: the view contains participating objects whose join structure and relationship characteristics must be defined.
Salesforce's current glossary defines a Logical View as a data object that combines multiple tables using special joins and then allows that enriched dataset to be queried as one object. It can subsequently participate in calculated fields, metrics, semantic relationships, and other definitions.
Proper cardinality is important because Tableau Semantics must know whether objects relate one-to-one, one- to-many, many-to-one, or many-to-many to prevent duplicate aggregation or missing results. Salesforce provides explicit cardinality configuration for semantic relationships.
Option B confuses a Logical View with ordinary semantic relationships inherited or defined among data objects. Option C describes neither the structure nor purpose of Logical Views.
The critical exam distinction is relationships preserve separate objects, while a Logical View combines objects into a single logical analytical structure.
References/Topics: Data Setup - > Tableau Semantics - > Logical Views - > Joins - > Cardinality.
NEW QUESTION # 34
A Tableau Next Consultant faces a requirement calling for data pivots, aggregations, and calculations to the data before pulling it into a semantic data model (SDM). Which Data 360 feature should the consultant use to create a data object with these data adjustments?
Answer: C
Explanation:
Batch Data Transforms are designed to perform repeatable data-shaping operations before downstream analytical consumption. Salesforce describes batch transforms as canvas-based processing flows capable of adding, manipulating, joining, aggregating, filtering, and applying formulas or other transformations to data before writing the resulting dataset to a target object.
This makes A the appropriate solution when the requirement involves multiple structural transformations- such as pivots, aggregations, and calculations-before the resulting dataset becomes part of a semantic model.
Identity Resolution solves a different problem: reconciling and matching records across data sources to build unified profiles. It does not provide the generalized transformation pipeline required here.
Calculated fields can create derived values, but they are insufficient when the preparation requirement involves broad reshaping and aggregation of the underlying dataset. The transformations should occur at the Data 360 data-preparation layer, after which the output object can be used by downstream analytics.
Batch transforms can write to supported DLO or DMO output nodes depending on the source and design, providing a persistent transformed object that semantic modeling and other Data 360 processes can consume.
References/Topics: Data Setup - > Data 360 - > Batch Data Transforms - > Aggregate, Join, Transform, and Output Nodes.
NEW QUESTION # 35
A team at Universal Containers collaborates frequently in Slack. The goal is to share a Tableau Next metric within a Slack canvas, ensuring it appears as a rich preview where the data regularly refreshes for each team member. All team members have appropriately assigned licenses. Which procedure should a Tableau Next Consultant instruct a user to follow to achieve this?
Answer: C
Explanation:
For a live Tableau Next preview inside a Slack canvas, Salesforce instructs users to paste the Tableau Next asset URL into the canvas and select Card from the Paste As menu. The resulting card can display a metric, dashboard, or visualization and regularly refresh its underlying data.
This behavior differs materially from sharing a static Tableau Next preview in an ordinary Slack channel or direct message. In a Canvas, Salesforce retrieves the data from each viewer's perspective. Consequently, each person must have appropriate Tableau Next access to the underlying asset and data. Unauthorized users receive an access message instead of seeing protected data. This preserves individual data governance while still providing a rich collaborative experience.
Option A does not describe the documented Slack Canvas workflow. Option C produces a static image and therefore fails the requirement for regularly refreshed information.
The exam distinction is important: standard Slack message preview = snapshot governed by sharing behavior; Slack Canvas Card = live, refreshable preview evaluated against each viewer's access.
References/Topics: Embedding, Cross-Cloud, and Interoperability - > Tableau Next in Slack - > Slack Canvas - > Paste As Card - > Viewer-Based Data Access.
NEW QUESTION # 36
A Tableau Next Consultant is asked to configure streaming data ingestion. Where is the output written?
Answer: C
Explanation:
A Data Stream Object (DSO) is the output associated with streaming data ingestion in this question.
Streaming ingestion is designed to continuously accept incoming records from a connected source and represent that stream in Data 360 so downstream processing and analytics can work with newly arriving data with minimal delay.
A Data Lake Object is used as a persisted data-layer object for broader ingestion and storage scenarios, while a Semantic Model is a downstream analytical abstraction that defines business-friendly measures, dimensions, relationships, metrics, and other semantic logic. Neither option describes the streaming-ingestion output requested by this item.
The exam distinction is to keep the ingestion pipeline separate from the semantic layer. Streaming data first enters through the streaming/data-stream construct, where the Data Stream Object represents the incoming stream. That data can then participate in downstream mapping, modeling, and Tableau Next analytics. The semantic model does not serve as the raw ingestion target; it consumes prepared and governed data structures to provide analytical meaning.
References/Topics: Data Setup - > Streaming Data Ingestion - > Data Stream Objects.
NEW QUESTION # 37
A Tableau Next Consultant is asked to configure workspace access for sensitive dashboards. Which General Access setting ensures no one can see dashboards by default?
Answer: B
Explanation:
Restricted is the correct General Access state. Salesforce documents that newly created Tableau Next assets are restricted by default. When General Access remains restricted, only individual users or Analytics Groups that have been explicitly granted access can open the asset.
For sensitive dashboards, this provides the correct baseline because access is opt-in rather than org-wide. The owner can then grant specific users or groups Viewer access and, where supported at the workspace level, Editor access.
Salesforce also makes an important distinction between Tableau Next asset access and underlying data access.
Even if a dashboard is shared with a user, that action does not automatically grant permission to the DLOs, DMOs, or data spaces on which the dashboard depends. Data 360 governance still applies separately.
"Public" and "Shared" are not the documented General Access states that produce the required private-by- default behavior. Current Tableau Next access management uses Restricted as the state in which only explicitly shared users or groups can access an asset.
For sensitive content, the architecture should therefore use Restricted asset access + explicit user/group sharing + Data 360 governance.
References/Topics: Managing Workspaces and Orgs - > General Access - > Restricted - > Asset Sharing - > Data Governance.
NEW QUESTION # 38
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