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NEW QUESTION # 14
A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the following levels of data access that they can be granted.
. Group 1: Viewers who cannot see any information on profitability
. Group 2: Viewers who can see profit and profit margin
. Group 3: Viewers who can see profit margin but not the value of profit Which approach should the consultant use to provide the required level of access?
Answer: B
Explanation:
The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.
References: The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation12. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.
NEW QUESTION # 15
In what way does View Acceleration improve performance?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
View Acceleration is a Tableau Server and Tableau Cloud feature that speeds up slow-loading views by precomputing their queries in the background.
According to Tableau's server and cloud performance documentation:
* When View Acceleration is enabled for a view, Tableau runs the queries behind that view on a background process and stores the results in memory.
* When an end user later opens the view, Tableau can serve the precomputed results immediately, rather than running the potentially long-running queries at that moment.
* This improves initial load time significantly for views that are slow because of heavy queries.
It does not only work with extract-based data sources (it can also help with many live connections), so option A is too limited.
It does not change the client-side rendering engine, so option C is incorrect.
It is not specific to transient functions but to any view where the query is expensive, so option D is not accurate.
Therefore, the correct description is that View Acceleration precompiles and fetches workbook data in a background process, which matches option B.
* Tableau Server and Tableau Cloud help describing View Acceleration as precomputing and caching view results using background processes.
* Performance tuning guidance recommending View Acceleration for views with slow query execution.
NEW QUESTION # 16
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which type of calculation is the consultant able to move?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau allows certain calculations to be materialized in extracts, meaning they are precomputed and stored inside the .hyper file to improve performance.
According to Tableau's extract documentation:
* Materializable calculations must be compatible with the extract engine and must not depend on dynamic, view-based, or post-query logic.
* Only row-level calculations and aggregation-level calculations without dependencies on runtime context can be materialized.
* Tableau cannot materialize any calculation containing:
* Table calculation functions
* Functions requiring post-aggregation logic
* View-dependent elements
* Parameters that need runtime evaluation
Evaluation of the choices:
A). A row-level calculation - Correct
Row-level calculations operate on each record individually before aggregation.
Tableau documentation specifies that these calculations can be pushed down into the extract and materialized because they do not depend on the visualization or user interaction.
Examples include concatenation, arithmetic, string manipulation, and row-based logic such as:
[Sales] * [Quantity] or IF [Region] = 'West' THEN 1 END
These can be precomputed inside the extract, improving performance.
B). A calculation that contains table calculation functions - Not allowed Table calculations (WINDOW_SUM, INDEX, RUNNING_SUM, RANK, etc.) depend on the table structure after aggregation and query execution.
Therefore, Tableau documentation states they cannot be materialized in extracts.
C). A calculation that contains parameters - Not allowed
Parameters are evaluated at runtime, meaning the user can change their value.
Because of this, Tableau cannot permanently compute and store such a calculation inside an extract.
D). A calculation that contains an aggregation - Generally not materialized Aggregated calculations often depend on query context and cannot always be materialized.
Only simple, context-free aggregations might be materialized, but Tableau explicitly warns that aggregations are not guaranteed candidates for extract materialization.
Thus, this is not the best answer compared to row-level logic.
Conclusion
Only row-level calculations meet Tableau's exact requirements for materialization in extracts.
References From Tableau Consultant Documentation
* Tableau Extract documentation describing materializable calculation types.
* Tableau guidance stating table calculations and parameter-dependent calculations cannot be materialized.
* Extract optimization guidelines describing row-level logic as eligible for materialization.
NEW QUESTION # 17
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?
Answer: D
Explanation:
For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:
* Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.
* Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices.
Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.
* Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.
References
This approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device- specific dashboards.
NEW QUESTION # 18
A consultant is working with a Tableau Server customer. The customer asks the consultant if there is a need to upgrade their instance of Tableau Server that was installed over 1 year ago.
Which two situations justify the need for an upgrade? Choose two.
Answer: B,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's upgrade guidance identifies two major drivers for upgrading Tableau Server:
* Version Compatibility with Tableau Desktop and Tableau Prep Builder
* Tableau Server must be equal to or newer than the version used by Desktop and Prep Builder for publishing.
* Organizations upgrading Desktop often must upgrade Server to avoid compatibility issues.
* This is a core reason to update a year-old installation.
* Security and Bug Fixes
* Tableau regularly publishes security patches, bug fixes, and stability enhancements.
* Older versions accumulate unresolved security issues that may be identified by security teams.
* Tableau explicitly states that upgrading ensures the instance receives the latest security protections.
Option B is incorrect because upgrading does not reduce hardware requirements; in many cases hardware needs may increase.
Option C is incorrect because Tableau Cloud features do not require upgrading Tableau Server. Tableau Cloud enhancements are independent of Server versions.
Therefore, the two conditions that justify upgrading are maintaining compatibility and addressing security vulnerabilities.
* Tableau's version compatibility matrix requiring alignment between Tableau Server and Desktop/Prep.
* Upgrade planning documents emphasizing security patches and bug fixes as key upgrade drivers.
* Notes describing that performance complaints alone are not solved simply by upgrading.
NEW QUESTION # 19
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