Optimize a model for performance in Power BI

Question 1

What is the primary goal of optimizing a Power BI model?

Answer 1

To enhance performance, usability, and scalability of the model for better report generation and data analysis.

Question 2

Why is it important to remove unnecessary columns and rows in your Power BI model?

Answer 2

To reduce the model size and improve query performance.

Question 3

What does defining and using appropriate relationships between tables ensure?

Answer 3

Accurate data analysis and efficient data retrieval.

Question 4

How can indexing in the data source improve model performance?

Answer 4

By speeding up query response times through efficient data retrieval.

Question 5

What role do aggregations play in optimizing a Power BI model?

Answer 5

They help to summarize detailed data, improving performance for large datasets.

Question 6

What is the purpose of the Performance Analyzer in Power BI?

Answer 6

To identify and diagnose performance bottlenecks in report elements such as visuals, measures, and relationships.

Question 7

What should be done before running the Performance Analyzer?

Answer 7

Clear both the visual cache and the data engine cache for accurate results.

Question 8

How can you clear the visual cache in Power BI Desktop?

Answer 8

Add a blank page, save, close, and reopen the file on the blank page.

Question 9

What categories are measured in the Performance Analyzer results?

Answer 9

DAX query, visual display, and other tasks.

Question 10

How can DAX query performance be optimized?

Answer 10

By using more efficient functions such as replacing FILTER with KEEPFILTERS.

Question 11

What is the impact of unnecessary columns on model performance?

Answer 11

They increase model size and refresh time, and should be removed if not needed.

Question 12

What role does metadata play in optimizing a Power BI model?

Answer 12

Ensuring metadata consistency and correctness improves semantic model performance and usability.

Question 13

What are the benefits of using variables in DAX formulas?

Answer 13

Improved performance, readability, simplified debugging, and reduced complexity.

Question 14

How do variables improve performance in DAX?

Answer 14

They prevent the need for Power BI to evaluate the same expression multiple times, making calculations more efficient.

Question 15

Why is readability improved by using variables?

Answer 15

Variables use short, self-describing names that replace complex expressions, making formulas easier to understand.

Question 16

How can variables assist in debugging DAX formulas?

Answer 16

By allowing evaluation of each variable separately, simplifying the identification of issues.

Question 17

Provide an example of a DAX formula using a variable for calculating year-over-year growth.
A:

Answer 17

Sales YoY Growth =
VAR SalesPriorYear = CALCULATE([Sales], PARALLELPERIOD(‘Date’[Date], -12, MONTH))
RETURN DIVIDE(([Sales] - SalesPriorYear), SalesPriorYear)

Question 18

What is cardinality in Power BI?

Answer 18

Cardinality describes the uniqueness of values in a column and the relationships between two tables.

Question 19

How does reducing cardinality benefit Power BI models?

Answer 19

Lower cardinality improves performance by reducing the data volume and enhancing query efficiency.

Question 20

What are the common cardinality types in Power BI relationships?

Answer 20

Many-to-one (:1), one-to-one (1:1), one-to-many (1:), and many-to-many (:).

Question 21

What is a technique to reduce model size in Power BI?

Answer 21

Using summary tables instead of detail tables to minimize data volume while summarizing necessary metrics.

Question 22

What is the impact of summarizing data on cardinality?

Answer 22

Summarizing data can significantly reduce cardinality, leading to improved model performance.

Question 23

What is DirectQuery in Power BI?

Answer 23

A method to connect directly to data in its source repository from within Power BI Desktop without importing the data.

Question 24

What factors affect DirectQuery performance?

Answer 24

Network latency, server performance, the number of users, and the complexity of queries.

Question 25

What are some best practices for optimizing DirectQuery models?

Answer 25

Reducing the number of visuals, minimizing columns and rows, creating appropriate indexes, and tuning the source database.

Question 26

What are the implications of using DirectQuery?

Answer 26

Potential performance issues, limitations on data transformation, security considerations, and limited modeling capabilities compared to imported data.

Question 27

What options can help reduce query load in Power BI Desktop?

Answer 27

Query reduction settings like disabling automatic interactions and adding apply buttons for slicers and filters.

Question 28

What is the primary purpose of using aggregations in Power BI?

Answer 28

To improve query performance by summarizing detailed data, reducing the amount of data that needs to be processed.

Question 29

How do aggregations work in Power BI?

Answer 29

Aggregations are pre-calculated summaries that the engine can use instead of querying the detailed data.

Question 30

What are the benefits of aggregations?

Answer 30

Reduced query times, improved report performance, and efficient data processing.

Question 31

What types of aggregations can be created in Power BI?

Answer 31

Summarizations like SUM, MIN, MAX, COUNT, and GROUP BY.

Question 32

When should you consider using aggregations?

Answer 32

When dealing with large datasets where detailed queries impact performance.