Section 1.1: Differentiating Star Schema from Snowflake Schema

One of the most fundamental concepts in Power BI is the distinction between Star Schema and Snowflake Schema.

Star Schema is characterized by a central fact table that includes quantitative performance metrics (like sales and revenue) surrounded by dimension tables that provide context (such as date, product, and customer). Below is a code snippet that showcases a simplified star schema using specific columns from a Sales table.

Snowflake Schema, on the other hand, is a variation in which dimension tables are normalized, breaking them down into sub-dimensions. In the following example, DimProduct illustrates a snowflake schema as it incorporates attributes from a product dimension and potentially additional normalized details.

Here’s how you can visualize both schemas through code snippets:

// Star Schema Example

FactSales =

SELECTCOLUMNS(Sales, "DateKey", Sales[DateKey], "ProductKey", Sales[ProductKey], "Amount", Sales[Amount])

// Snowflake Schema Example

DimProduct =

SELECTCOLUMNS(Products, "ProductKey", Products[ProductKey], "CategoryKey", Products[CategoryKey], "ProductName", Products[ProductName])

Section 1.2: Handling Slowly Changing Dimensions (SCDs)

Slowly Changing Dimensions (SCDs) are essential for managing changes in dimension data over time. Type 2 SCDs, in particular, keep a historical record of changes. The following code demonstrates how to modify a dimension table to accommodate SCD Type 2 by integrating columns such as ValidFrom and ValidTo.

// Handling SCD Type 2

DimCustomer =

Table.DimCustomers

<<

SCD.Transform(

{

{"CustomerID", SCD.Type2}

},

{

{"ValidFrom", "StartDate"},

{"ValidTo", "EndDate"}

},

"SCD"

)

Section 1.3: Understanding Row-Level Security (RLS)

Row-Level Security (RLS) plays a vital role in restricting data access based on user identity. The following DAX code snippet filters the 'Sales' table, displaying only the data corresponding to the user's region, thereby enforcing RLS.

// Row-Level Security

SecurityTable =

FILTER('Sales', 'Sales'[Region] = USERPRINCIPALNAME())

Section 2.1: Measures vs. Calculated Columns

In Power BI, measures are dynamic calculations executed on demand, usually with DAX functions, yielding aggregated values like total sales. Conversely, calculated columns are computed during data loading, resulting in new columns within a table. Below are examples of each:

// Measure Example

TotalSales = SUM('Sales'[Amount])

// Calculated Column Example

RevenuePerUnit = 'Products'[Revenue] / 'Products'[UnitsSold]

Section 2.2: Performance Optimization Techniques

Optimizing performance in Power BI often involves pre-calculating and storing aggregated data, which can significantly reduce query times. Here’s how you can utilize SUMMARIZECOLUMNS for creating an aggregation query:

// Aggregations

SUMMARIZECOLUMNS (

'Date'[Year],

'Product'[Category],

"Total Sales", [Total Sales]

)

Section 2.3: Understanding Joins in Power Query

In Power Query, INNER JOIN returns only the rows with matches in both tables, while LEFT JOIN includes all rows from the left table and the corresponding rows from the right table, filling in nulls where there are no matches. Here’s a code snippet for both types of joins:

// INNER JOIN

ResultTable = Table.NestedJoin(Table1, {"ID"}, Table2, {"ID"}, "JoinedTable")

// LEFT JOIN

ResultTable = Table.LeftJoin(Table1, {"ID"}, Table2, {"ID"})

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Section 2.4: Time Intelligence Functions in DAX

Time intelligence functions in DAX empower you to analyze data across different time periods. The following code illustrates a Year-to-Date calculation:

// Year-to-Date Calculation

YTD Sales = TOTALYTD(SUM('Sales'[Amount]), 'Date'[Date])

Section 2.5: Role-Playing Dimensions Explained

Role-playing dimensions occur when the same dimension table is utilized in various contexts. This snippet demonstrates creating separate 'OrderDate' and 'ShipDate' tables from the same 'Date' dimension:

// Role-Playing Dimensions

OrderDate =

SELECTCOLUMNS(Sales, "OrderDate", Sales[OrderDate])

ShipDate =

SELECTCOLUMNS(Sales, "ShipDate", Sales[ShipDate])

Section 2.6: Import vs. DirectQuery Modes

Understanding the differences between Power BI's Import Mode and DirectQuery Mode is crucial. Import Mode loads data into Power BI’s internal model for quicker query performance, while DirectQuery queries the data source in real-time. Here’s a comparison:

// Import Mode

SELECTCOLUMNS('Sales', "OrderDate", 'Sales'[OrderDate])

// DirectQuery Mode

SELECTCOLUMNS('Sales', "OrderDate", 'Sales'[OrderDate])

Section 2.7: Managing Errors and Data Quality

To ensure data quality, handling errors is essential. The following snippet shows how to manage data type errors during transformations in Power Query:

// Handling Errors

CleanedData =

Table.TransformColumnTypes(SourceData, {

{"Amount", type number}

}, MissingField.Ignore)

With these insights and code examples, you’ll be well-equipped to handle the most challenging Power BI data modeling interview questions. Remember, thorough practice and a strong grasp of concepts are vital for success.

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