Practice Free Analytics-Con-301 Exam Online Questions
A client has a dashboard that renders in less than 10 seconds. The client receives a request to add a new calculated field that will return TRUE if a Project contains any one of the values "Project 1" or "Project 2" and FALSE otherwise. After adding the function found below, the dashboard’s render time increases to 14 seconds from 10 seconds.
[Project] = ‘Project 1’ OR [Project] = ‘Project 2’
Which function should the consultant use to reduce the render time?
- A . [Project] = ‘Project 1’ AND [Project] = ‘Project 2’
- B . [Project] IN (‘Project 1’ OR ‘Project 2’)
- C . (([Project] = ‘Project 1’) OR ([Project] = ‘Project 2’))
- D . [Project] IN (‘Project 1’, ‘Project 2’)
D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau documentation states that IN expressions are optimized internally for better query performance than multiple OR statements. They are pushed efficiently to the data source and simplified during query compilation.
The original expression uses two OR conditions, which increases the complexity of row-level evaluation:
[Project] = ‘Project 1’ OR [Project] = ‘Project 2’
Tableau’s performance guidelines recommend replacing multiple OR comparisons with an IN expression whenever possible:
[Project] IN (‘Project 1’, ‘Project 2’)
This reduces rendering time by minimizing row evaluation overhead and creating a cleaner, optimized logical condition.
Option D achieves this exactly.
Option A is logically incorrect because a project cannot simultaneously equal both values.
Option B is syntactically incorrect because IN expects a list, not an OR inside the list.
Option C is simply a reformatting of the original OR expression and provides no performance improvement.
Option D is the only valid, optimized solution.
Tableau calculation optimization guidance recommending IN over OR for performance.
Tableau’s query performance notes indicating that OR statements expand logical branches and slow down evaluation.
Best practices for row-level calculations suggesting simplified logical expressions.
An executive-level workbook leverages 37 of the 103 fields included in a data source. Performance for the workbook is noticeably slower than other workbooks on the same Tableau Server.
What should the consultant do to improve performance of this workbook while following best practice?
- A . Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.
- B . Connect to the data source via a custom SQL query.
- C . Use filters, hide unused fields, and aggregate values.
- D . Restrict users from accessing the workbook to reduce server load.
C
Explanation:
To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.
Reference: The best practices for optimizing workbook performance in Tableau are well-documented in Tableau’s official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance12.
A Tableau Cloud client has requested a custom dashboard to help track which data sources are used most frequently in dashboards across their site.
Which two actions should the client use to access the necessary metadata? Choose two.
- A . Connect directly to the Site Content data source within the Admin Insights project.
- B . Query metadata through the GraphiQL engine.
- C . Access metadata through the Metadata API.
- D . Download metadata through Tableau Catalog.
B,C
Explanation:
To track which data sources are used most frequently across a site in Tableau Cloud, the client should use the GraphiQL engine and the Metadata API. The GraphiQL engine allows for interactive exploration of the metadata, making it easier to construct and test queries1. The Metadata API provides access to metadata and lineage of external assets used by the content published to Tableau Cloud, which is essential for tracking data source usage2.
Reference: The actions are based on the capabilities of the GraphiQL engine and the Metadata API as described in Tableau’s official documentation and learning resources321.