Optimize a model for performance in Power BI Flashcards
What is the primary goal of optimizing a Power BI model?
To enhance performance, usability, and scalability of the model for better report generation and data analysis.
Why is it important to remove unnecessary columns and rows in your Power BI model?
To reduce the model size and improve query performance.
What does defining and using appropriate relationships between tables ensure?
Accurate data analysis and efficient data retrieval.
How can indexing in the data source improve model performance?
By speeding up query response times through efficient data retrieval.
What role do aggregations play in optimizing a Power BI model?
They help to summarize detailed data, improving performance for large datasets.
What is the purpose of the Performance Analyzer in Power BI?
To identify and diagnose performance bottlenecks in report elements such as visuals, measures, and relationships.
What should be done before running the Performance Analyzer?
Clear both the visual cache and the data engine cache for accurate results.
How can you clear the visual cache in Power BI Desktop?
Add a blank page, save, close, and reopen the file on the blank page.
What categories are measured in the Performance Analyzer results?
DAX query, visual display, and other tasks.
How can DAX query performance be optimized?
By using more efficient functions such as replacing FILTER with KEEPFILTERS.
What is the impact of unnecessary columns on model performance?
They increase model size and refresh time, and should be removed if not needed.
What role does metadata play in optimizing a Power BI model?
Ensuring metadata consistency and correctness improves semantic model performance and usability.
What are the benefits of using variables in DAX formulas?
Improved performance, readability, simplified debugging, and reduced complexity.
How do variables improve performance in DAX?
They prevent the need for Power BI to evaluate the same expression multiple times, making calculations more efficient.
Why is readability improved by using variables?
Variables use short, self-describing names that replace complex expressions, making formulas easier to understand.
How can variables assist in debugging DAX formulas?
By allowing evaluation of each variable separately, simplifying the identification of issues.
Provide an example of a DAX formula using a variable for calculating year-over-year growth.
A:
Sales YoY Growth =
VAR SalesPriorYear = CALCULATE([Sales], PARALLELPERIOD(‘Date’[Date], -12, MONTH))
RETURN DIVIDE(([Sales] - SalesPriorYear), SalesPriorYear)
What is cardinality in Power BI?
Cardinality describes the uniqueness of values in a column and the relationships between two tables.
How does reducing cardinality benefit Power BI models?
Lower cardinality improves performance by reducing the data volume and enhancing query efficiency.
What are the common cardinality types in Power BI relationships?
Many-to-one (:1), one-to-one (1:1), one-to-many (1:), and many-to-many (:).
What is a technique to reduce model size in Power BI?
Using summary tables instead of detail tables to minimize data volume while summarizing necessary metrics.
What is the impact of summarizing data on cardinality?
Summarizing data can significantly reduce cardinality, leading to improved model performance.
What is DirectQuery in Power BI?
A method to connect directly to data in its source repository from within Power BI Desktop without importing the data.
What factors affect DirectQuery performance?
Network latency, server performance, the number of users, and the complexity of queries.
