Data model best practices in Tableau - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Star Schema in Tableau

Which of the following best describes a star schema data model in Tableau?

A Dimension tables connected in a circular loop to the fact table.

B A central fact table connected to multiple dimension tables, resembling a star shape.

C A single large table with all data combined without relationships.

D Multiple fact tables connected directly to each other without dimension tables.

💡 Hint

Think about how fact and dimension tables relate in a simple, clear structure.

A star schema has one central fact table linked to several dimension tables, making queries efficient and easy to understand.

❓ lod_result

intermediate

Calculating Total Sales with Correct Granularity

Given a sales fact table with columns OrderID, ProductID, and SalesAmount, and a product dimension table with ProductID and Category, which LOD expression correctly calculates total sales by category without double counting?

Tableau

Total Sales = SUM([Sales].[SalesAmount])

A Total Sales by Category = { FIXED : SUM([Sales].[SalesAmount]) }

B Total Sales by Category = { FIXED [Product].[ProductID] : SUM([Sales].[SalesAmount]) }

C Total Sales by Category = SUM([Sales].[SalesAmount])

D Total Sales by Category = { FIXED [Product].[Category] : SUM([Sales].[SalesAmount]) }

💡 Hint

Use a function that keeps the category filter but removes other filters on the product table.

FIXED keeps the filter on Category while removing other filters, ensuring correct aggregation by category.

❓ visualization

advanced

Choosing the Best Visualization for Hierarchical Data

You have a data model with sales data by region, country, and city. Which Tableau visualization best helps users explore this hierarchy interactively?

A A treemap with drill-down capability from region to city.

B A simple bar chart showing total sales by city only.

C A pie chart showing sales by region without hierarchy.

D A scatter plot with sales on X-axis and profit on Y-axis.

💡 Hint

Look for a visualization that shows parts within parts and allows drilling down.

A treemap with drill-down lets users explore hierarchical data easily by clicking through levels.

🔧 Formula Fix

advanced

Fixing Relationship Cardinality Issues

You created a relationship between a customer dimension and sales fact table in Tableau, but your sales totals are unexpectedly high. What is the most likely cause?

A The relationship cardinality is set to many-to-many instead of one-to-many.

B The sales fact table is missing a primary key.

C The customer dimension table has duplicate customer IDs.

D The relationship is set to one-to-one but should be many-to-one.

💡 Hint

Check how many records in each table relate to the other.

Many-to-many relationships can cause duplication in aggregations, inflating totals.

🎯 Scenario

expert

Optimizing Data Model Performance for Large Datasets

Your Tableau dashboard is slow because the data model has many large tables joined with complex relationships. Which approach will best improve performance while keeping data accuracy?

A Remove all relationships and use cross-database joins instead.

B Load all data as live connections without any filters or aggregations.

C Use extract filters to reduce data volume and create aggregated tables for common queries.

D Duplicate all tables and join them multiple times to speed up queries.

💡 Hint

Think about reducing data size and pre-aggregating to speed up queries.

Extract filters and aggregated tables reduce data volume and complexity, improving dashboard speed without losing accuracy.