Star schema concept in SQL - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the Fact Table in a Star Schema

In a star schema, which table typically contains the measurable, quantitative data for analysis?

A Staging table

B Fact table

C Lookup table

D Dimension table

💡 Hint

Think about where the numbers you want to analyze are stored.

The fact table stores measurable data such as sales amounts or quantities. Dimension tables provide context like dates or product details.

❓ data_modeling

intermediate

Choose the Correct Star Schema Structure

Which of the following SQL table structures best represents a star schema for sales data?

A One large table with all sales and product details combined

B Multiple normalized tables with many-to-many relationships between sales and products

C A central sales fact table linked to separate product, customer, and date dimension tables

D A single table with repeated product and customer information for each sale

💡 Hint

Star schema uses a central fact table connected to dimension tables.

Star schema organizes data with a central fact table linked to dimension tables, simplifying queries and improving performance.

❓ dax_lod_result

advanced

Calculate Total Sales Using DAX in a Star Schema

Given a fact table 'Sales' with a column 'Amount', which DAX measure correctly calculates the total sales amount?

SQL

Total Sales = SUM(Sales[Amount])

A Total Sales = SUMX(Sales, Sales[Quantity])

B Total Sales = COUNT(Sales[Amount])

C Total Sales = AVERAGE(Sales[Amount])

D Total Sales = SUM(Sales[Amount])

💡 Hint

To get total sales, add up all amounts.

SUM aggregates the total of the Amount column. COUNT counts rows, AVERAGE finds mean, SUMX sums an expression.

❓ visualization

advanced

Best Visualization for Star Schema Sales Data

You have sales data in a star schema with dimensions for Product, Date, and Customer. Which visualization best shows total sales by product category over time?

A Line chart showing sales trends by product category over months

B Table listing all sales transactions

C Pie chart showing sales by product category

D Scatter plot of sales amount vs. customer age

💡 Hint

Think about showing changes over time by category.

A line chart effectively shows trends over time for different categories, making it ideal for this data.

🔧 Debug

expert

Identify the Error in Star Schema SQL Query

Consider this SQL query joining a fact table 'Sales' with dimension 'Product' to get total sales per product name:

SELECT Product.Name, SUM(Sales.Amount) AS TotalSales
FROM Sales
JOIN Product ON Sales.ProductID = Product.ID
GROUP BY Product.Name, Sales.Amount;

What is the error in this query?

A Grouping by Sales.Amount causes incorrect aggregation

B Missing WHERE clause to filter sales data

C JOIN condition uses wrong columns

D SUM function cannot be used with GROUP BY

💡 Hint

Check which columns should be in GROUP BY when using aggregation.

Grouping by Sales.Amount breaks aggregation because Amount is a measure. Only group by Product.Name to sum amounts correctly.

Practice

1. What is the main characteristic of a star schema in a data warehouse?

easy

A. One central fact table connected to multiple dimension tables

B. Multiple fact tables connected to one dimension table

C. Only dimension tables without any fact tables

D. A flat table with no relationships

Solution

1. Step 1: Understand star schema structure

   A star schema has a central fact table that stores measurable data.

2. Step 2: Identify dimension tables role

   Dimension tables surround the fact table and provide descriptive attributes.

3. Final Answer:

   One central fact table connected to multiple dimension tables -> Option A

4. Quick Check:

   Star schema = central fact + many dimensions [OK]

Hint: Fact table is the center, dimensions are spokes [OK]

Common Mistakes:

• Confusing fact and dimension tables
• Thinking star schema has multiple fact tables
• Assuming no relationships exist

2. Which SQL JOIN type is typically used to connect fact and dimension tables in a star schema?

easy

A. INNER JOIN

B. LEFT JOIN

C. FULL OUTER JOIN

D. CROSS JOIN

Solution

1. Step 1: Understand fact-dimension relationship

   Fact tables usually have matching keys in dimension tables.

2. Step 2: Choose JOIN type for matching rows

   INNER JOIN returns only matching rows, ideal for star schema queries.

3. Final Answer:

   INNER JOIN -> Option A

4. Quick Check:

   Fact and dimension use INNER JOIN [OK]

Hint: Use INNER JOIN to link matching keys [OK]

Common Mistakes:

• Using CROSS JOIN which multiplies rows
• Using FULL OUTER JOIN causing unnecessary nulls
• Using LEFT JOIN when INNER JOIN suffices

3. Given a star schema with a fact table Sales and dimension table Product, what does this SQL query return?

SELECT p.ProductName, SUM(s.SalesAmount) AS TotalSales
FROM Sales s
JOIN Product p ON s.ProductID = p.ProductID
GROUP BY p.ProductName;

medium

A. Sales amount grouped by sales date

B. Total sales amount for each product

C. Total sales amount for all products combined

D. List of products without sales data

Solution

1. Step 1: Analyze JOIN and GROUP BY

   The query joins Sales and Product on ProductID and groups results by product name.

2. Step 2: Understand aggregation

   SUM(s.SalesAmount) calculates total sales per product group.

3. Final Answer:

   Total sales amount for each product -> Option B

4. Quick Check:

   GROUP BY product sums sales per product [OK]

Hint: GROUP BY product sums sales per product [OK]

Common Mistakes:

• Ignoring GROUP BY causing total for all products
• Confusing JOIN keys
• Assuming query lists products without sales

4. Identify the error in this SQL query using a star schema:

SELECT d.CustomerName, SUM(f.SalesAmount)
FROM FactSales f
JOIN DimProduct d ON f.ProductID = d.ProductID
GROUP BY d.CustomerName;

medium

A. JOIN uses wrong dimension table alias

B. GROUP BY column not in SELECT

C. SUM aggregation missing alias

D. JOIN condition uses wrong keys

Solution

1. Step 1: Check JOIN condition keys

   FactSales.ProductID should join with DimProduct.ProductID, but SELECT and GROUP BY use CustomerName from a different dimension.

2. Step 2: Identify mismatch in dimension usage

   CustomerName likely belongs to DimCustomer, not DimProduct, so JOIN keys are incorrect.

3. Final Answer:

   JOIN condition uses wrong keys -> Option D

4. Quick Check:

   JOIN keys must match correct dimension [OK]

Hint: Match JOIN keys to correct dimension table [OK]

Common Mistakes:

• Grouping by column not selected
• Using alias inconsistently
• Missing aggregation alias

5. You want to design a star schema for sales data including customers, products, and time. Which approach best supports fast queries and easy analysis?

hard

A. Store all data in one big table without relationships

B. Create multiple fact tables for each dimension and one large dimension table

C. Create one fact table for sales and separate dimension tables for customers, products, and time

D. Use normalized tables with many joins instead of star schema

Solution

1. Step 1: Understand star schema design principles

   Star schema uses one fact table for measurable events and multiple dimension tables for descriptive data.

2. Step 2: Evaluate options for query speed and simplicity

   Create one fact table for sales and separate dimension tables for customers, products, and time matches star schema best, enabling fast joins and simple queries.

3. Final Answer:

   Create one fact table for sales and separate dimension tables for customers, products, and time -> Option C

4. Quick Check:

   Star schema = 1 fact + many dimensions [OK]

Hint: One fact table, many dimension tables for star schema [OK]

Common Mistakes:

• Using multiple fact tables unnecessarily
• Flattening all data into one table
• Choosing normalized schema over star schema for BI