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Quizzes > Quizzes for Business > Technology

Tableau Performance Optimization Quiz Challenge

Test Your Skills in Tableau Efficiency Tuning

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art illustrating a Tableau Performance Optimization Quiz.

Ready to sharpen your dashboard performance skills? This Tableau Performance Optimization Quiz is ideal for BI analysts, data enthusiasts, and developers looking to boost their workbook speed and efficiency. Over 15 multiple-choice questions cover query optimization, extract tuning, and best design practices, ensuring you gain critical insights into Tableau efficiency. You can freely modify the quiz content in our editor to create custom challenges or combine questions from related tests like the JMeter Performance Testing Quiz and Performance Indicators Knowledge Quiz . Discover more exciting quizzes to continue honing your data visualization expertise.

Which Tableau feature allows you to capture and review workbook performance metrics?
Performance Recording
Data Interpreter
Dashboard Starters
Data Source Filters
Performance Recording captures detailed timing information for workbook operations so you can analyze and optimize performance. The other options do not provide timing metrics for queries or rendering.
What file extension does Tableau use for the newer extract format?
.hyper
.tde
.csv
.twb
.hyper is the modern Tableau extract format that replaced the older .tde engine for improved performance and scalability. The .tde format is legacy, while .csv and .twb are not extract formats.
Which filter type in Tableau creates a temporary table to speed up the evaluation of subsequent filters?
Context Filter
Quick Filter
Data Source Filter
Extract Filter
A context filter builds a temporary table of relevant rows that later filters reference, reducing the data scanned for additional filters. Quick filters and data source filters do not create this intermediate table.
What is one benefit of using extracts instead of live connections in Tableau?
Reduced network latency and faster query performance
Real-time data always guaranteed
Unlimited data freshness
No need to refresh extracts
Extracts store a snapshot of data locally in an optimized, columnar format which reduces network latency and speeds up queries. Live connections provide real-time data but can be slower due to network and database load.
Which practice can help improve dashboard load times?
Reduce the number of quick filters
Increase workbook size
Add high-resolution images
Use multiple data sources unnecessarily
Quick filters generate extra queries and metadata, so minimizing them reduces rendering time. Increasing workbook size or adding high-resolution images typically slows dashboards, and extra data sources add overhead.
When should you use an incremental extract refresh rather than a full refresh?
When only new rows are needed and data volume is large
When schema changes on the source
For small static datasets
To capture all historical data every time
Incremental refreshes only append new records and avoid reprocessing the entire dataset, which is ideal for large tables where history remains unchanged. Full refreshes reload all data and are less efficient when history can be retained.
Which calculation style tends to be faster because processing is done at the database level?
Aggregate calculation on indexed fields
Table Calculation
Level of Detail calculation
Custom SQL
Aggregate calculations can often be pushed down to the database where indexes and optimizations accelerate performance. Table calculations and LOD expressions are processed in Tableau's engine and can be slower.
Why can custom SQL queries slow down Tableau performance?
Because Tableau wraps the SQL and executes it for every view load, preventing query optimization
Because custom SQL caches all results locally
They automatically convert to extracts
They force Tableau to use extract over live
Custom SQL often runs as a subquery for each view, which can bypass the database's native optimization paths and lead to slower performance. Tableau does not automatically cache or extract custom SQL queries.
What does the "Query Text" column in the performance recording workbook display?
The actual SQL sent to the database
The original data source name
The number of rows returned
The filters applied
The Query Text shows the exact SQL statements Tableau issues to the database, which helps diagnose inefficiencies. Data source names, row counts, and filter details appear elsewhere in the performance workbook.
How can using 'Only Relevant Values' on quick filters improve performance?
It limits filter values based on other filter selections, reducing query complexity
It shows all values up front
It disables filter caching
It forces a full refresh of data source
'Only Relevant Values' restricts the filter domain to values valid after other filters, lowering the workload for subsequent queries. Showing all values can be expensive on high-cardinality fields.
Which of the following can help reduce the data transferred from the source?
Apply an extract filter to exclude unused data
Use multiple dimensions in rows shelf
Add more calculated fields
Use live connection for all extracts
Extract filters remove unnecessary rows at extract creation, reducing the volume of data Tableau needs to process. Adding dimensions or fields typically increases data movement, and live connections don't reduce transferred data.
What is a recommended way to optimize dashboard rendering when using multiple worksheets?
Combine multiple views into one worksheet using Measure Names/Measure Values
Place each view on separate dashboard zones
Use live data for each worksheet
Increase dashboard size
Combining views into a single worksheet reduces the number of separate queries and rendering passes, speeding up load time. Separate worksheets generate independent queries which add overhead.
In Tableau, what is the primary purpose of the Performance Recorder tool?
To identify and analyze time taken by different actions in a workbook
To optimize SQL queries automatically
To adjust server memory allocation
To update extracts in the background
The Performance Recorder logs timing for events like Query, Compute, and Layout, helping you pinpoint slow operations. It does not automatically tune SQL or manage server resources.
Which of these is a best practice to improve join performance when blending data sources?
Use the least number of fields in the join clause and ensure indexing
Always join on text fields
Blend on high cardinality fields only
Use multiple secondary sources
Minimizing join fields, especially on indexed numeric keys, reduces query complexity and enhances performance. Text fields and high cardinality joins are more expensive.
Where can you find detailed logs about query execution and performance in Tableau Desktop?
In the 'Logs' folder in the Tableau repository directory
In the 'My Documents' folder
In the Windows Event Viewer
In the data source connection dialog
Tableau Desktop writes performance and query details to log files located under the Logs folder in your Tableau repository. Other locations do not store these detailed logs.
After reviewing a performance recording, you see that 'Query' events dominate the timeline. Which action is most effective to reduce query times?
Create a data extract or add appropriate indexes to the underlying database
Increase the number of worksheets in the dashboard
Use more floating objects
Hide unused fields in Tableau Extract
Extracts or database indexes speed up query execution by reducing data scanned and improving retrieval paths. Adding worksheets or floating objects has no effect on query performance.
You have a high-cardinality quick filter that is slow. Which method will most significantly improve its performance?
Convert it to a parameter or use a context filter to pre-aggregate data
Increase the number of filter values displayed
Use a floating container for the filter
Switch to a live connection
Parameters incur no additional database queries and context filters limit data before the quick filter is applied. Displaying more values or floating layout changes do not impact query efficiency.
Which order of operations correctly reflects when extract filters are applied relative to data source and context filters?
Extract filters > Data source filters > Context filters
Data source filters > Extract filters > Context filters
Context filters > Extract filters > Data source filters
Data source filters > Context filters > Extract filters
Extract filters run at extract creation, removing unwanted rows first. Data source filters apply next at connection time, and context filters evaluate before other filters at runtime.
In a blended data scenario, what limits performance and how can you alleviate the issue?
Secondary data sources issue individual queries per primary row; reducing data in the primary via extract or pre-aggregation improves performance
Blended data stores everything locally; use live connection
Blends always use full dataset; set up incremental extract
Use LOD expressions instead of blending
In blending, Tableau issues separate queries against the secondary source for each primary row, so reducing primary data volume or pre-aggregating it cuts query counts. The other options do not address the root cause.
When designing a dashboard for optimal performance, which layout practice is recommended?
Use tiled containers and fixed-size layouts to reduce rendering complexity
Use only floating objects with auto-sizing
Set dashboard size to automatic to fit content
Embed as many sheets as possible
Tiled containers and fixed sizes enable Tableau to predict rendering boundaries and reduce layout calculations. Floating objects and automatic sizing increase computation overhead.
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Learning Outcomes

  1. Analyse Tableau workbook elements for performance issues
  2. Identify slow-running queries and dashboard bottlenecks
  3. Apply best practices to optimize data extracts and connections
  4. Evaluate the impact of filters and calculations on speed
  5. Demonstrate use of performance recording and monitoring tools
  6. Master techniques for efficient dashboard design

Cheat Sheet

  1. Optimize Data Extracts - Extracting only the necessary data and hiding unused fields can dramatically boost dashboard load times and improve interactivity. Pre-aggregating records and switching to Hyper extracts reduces query complexity and speeds up analytics. This ensures you're working with a lean dataset that's quick to refresh and visualize. Read full tips on Tableau's blog
  2. Limit the Number of Marks - Visualizations with fewer data points render much faster, keeping students engaged rather than waiting for charts to load. By removing unnecessary dimensions or grouping values, you'll maintain clarity and speed. Guided drill-downs let you explore detail on demand without overloading initial views. Discover how to balance detail and speed
  3. Efficient Use of Filters - Filters are powerful, but too many or the wrong type can slow things down. Prefer include filters over exclude, and use continuous date filters instead of discrete ones to optimize query performance. Thoughtful filter design ensures students can slice data swiftly without dragging the system. Master filter best practices
  4. Simplify Calculations - Nested or complex string computations can bog down processing time. Whenever possible, push heavy calculations back to your database and stick with numeric or Boolean fields for in-tool formulas. This keeps your workbook light and your insights delivered at the speed of thought. Learn to streamline your calculations
  5. Optimize Joins and Blends - Each join adds overhead; limiting them and using numeric keys makes queries snappier. Avoid custom SQL subqueries in Tableau and instead join tables directly or prepare data upstream. Clean, well-indexed sources mean students experience seamless blending of multiple datasets. Explore data source optimization
  6. Use Performance Recording Tools - Tableau's built-in Performance Recorder pinpoints slow queries, rendering delays, and worksheet bottlenecks. Analyzing these recordings teaches you exactly where to focus your tuning efforts. Regular checkups keep dashboards running smoothly as your data grows. Dive into Performance Recorder
  7. Design Efficient Dashboards - Limit dashboard sheets to only those that add value and lock container sizes to avoid dynamic resizing hiccups. Using tiled layouts instead of floating objects reduces rendering complexity. A clean, consistent design helps students navigate insights without distraction or delay. Step-by-step dashboard tuning guide
  8. Reduce Dashboard Scope - Overcrowded dashboards are confusing and slow. Delete unused worksheets, consolidate similar views, and remove extra data sources. A focused, purpose-driven layout makes learning smoother and keeps performance crisp. Keep dashboards lean and clean
  9. Utilize Indexing - Assigning indexes to your filters and join keys accelerates database lookups dramatically. Proper indexing sorts records at the source, ensuring queries return results in milliseconds. Students will notice the difference when exploring large datasets interactively. See indexing strategies
  10. Monitor and Test Performance - Keep an eye on query response times and dashboard load speeds to catch slowdowns early. Conducting load tests under varying conditions helps you understand how your visualizations perform at scale. Proactive monitoring means you can fix issues before they impact learning. Understand performance monitoring
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