COURSE 4 – DATA MODELING IN POWER BI

Module 3: Optimize a Model for Performance in Power BI

MICROSOFT POWER BI DATA ANALYST PROFESSIONAL CERTIFICATE

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TABLE OF CONTENT

INTRODUCTION – Optimize a model for performance in Power BI

This module delves into the optimization process within Power BI, thoroughly examining the various tools and methods available to enhance performance. It covers the use of performance analyzer and DirectQuery features, demonstrating how these tools can be effectively employed to achieve optimal results.

Additionally, the module provides an in-depth exploration of Data Analysis Expressions (DAX), focusing on its practical applications and real-world usage. By integrating these advanced features and techniques, the module aims to equip learners with the knowledge and skills needed to optimize Power BI performance and leverage DAX for sophisticated data analysis and business intelligence tasks.

Learning Objectives

  • Identify the need for performance optimization.
  • Optimize performance in a Power BI model.
  • Optimize performance in DAX queries.

SELF-REVIEW: IMPROVING DATA MODEL PERFORMANCE

1. In the scenario presented in this exercise, what is the purpose of adjusting the Cross-filter direction to Single in Power BI?

  • To enable the addition of more data fields to the model.
  • To manage the storage space required for the data model.
  • To allow for the implementation of many-to-many relationships in the model.
  • To reduce the complexity of the model and limit the direction in which filters are applied. (CORRECT)

Correct! Setting the Cross-filter direction to Single in Power BI, simplifies the data model and optimizes the filter functionality. This setting directs the filter to operate in one direction only. This can simplify the analysis and interpretation of results.

2. Based on your observation of the first ten records, which order has the highest Order Total value?

  • Order with Order ID 2003. (CORRECT)
  • Order with Order ID 2007.
  • Order with Order ID 2001.
  • Order with Order ID 2005.

Correct! The order identified by Order ID 2003 carries the highest Order Total value of $5400 amongst the first ten records. This could result from various factors such as the quantity of goods ordered, the individual cost of each item, and the application of any taxes, fees, or discounts. It also suggests efficient transaction processing and could indicate a high-value customer.

3. In the scenario presented in the exercise, why is it beneficial to modify the Customers and Orders relationship from a many-to-many relationship to a one-to-many relationship in Power BI?

  • To improve the visual readability of the data model.
  • To enhance the performance of the data model. (CORRECT)
  • To ensure that each customer has only one order.

Correct! Modifying the relationship to one-to-many can improve performance because one-to-many relationships are simpler and faster for Power BI to navigate when loading data and calculating results. One-to-many relationships are simpler and less resource-intensive for Power BI to navigate and process than many-to-many relationships.

4. What is the primary aim of optimization in the context of Power BI?

  • To collect more data for future analysis to assist with models and reports.
  • To reduce the complexity of data visualization techniques when working with models.
  • To achieve the best performance of data models, reports, and dashboards. (CORRECT)

Correct! The main goal of optimization in Power BI is to ensure the best performance of data models, reports, and dashboards, particularly when dealing with large volumes of data.

5. What is the primary function of the filtering optimization technique in Power BI?

  • To visualize data more effectively to ensure that it is clear and relevant.
  • To standardize the format of the data to enhance its clarity and relevance.
  • To enhance the clarity and relevance of data by removing unnecessary noise. (CORRECT)

Correct! Filtering in Power BI is used to remove irrelevant data or ‘noise’, leaving only the relevant data behind.

6. Which of the following factors contribute to performance issues when working with many-to-many relationships in Power BI? Select all that apply. 

  • The presence of large volumes of data. (CORRECT)
  • The number of calculated columns.
  • The complexity of the data model. (CORRECT)
  • The use of bi-directional filters. (CORRECT)

Correct! When handling large amounts of data in many-to-many relationships, performance issues can become more evident.

Correct! Many-to-many relationships inherently add to the complexity of the data model, which can affect performance.

Correct! While bi-directional filters can increase complexity and cause performance issues, the core concern is the added intricacy they introduce to the data model.

7. How does high cardinality impact the performance of your Power BI reports?

  • It slows down the processing of queries. (CORRECT)
  • It simplifies the structure of the data.
  • It enhances the report’s responsiveness.

Correct! When you have high cardinality, it can increase the size of your data model and the time taken to process queries, slowing down your Power BI reports.

KNOWLEDGE CHECK: OPTIMIZE A MODEL FOR PERFORMANCE IN POWER BI

1. How does performance optimization primarily improve decision-making?

  • By enabling faster analysis of trends through swiftly loading reports. (CORRECT)
  • By simplifying complex DAX calculations, making it easier to interpret data.
  • By making Microsoft Power BI reports more visually appealing.
  • By enhancing user experience. 

Correct! Performance optimization enables faster analysis by ensuring that reports load quickly, which aids in timely and informed decision-making. The process of optimization involves a series of modifications, tuning, and streamlining of data models, reports, and dashboards with the intent to boost performance.

2. How does indexing optimize the data analysis process in Microsoft Power BI?

  • By organizing data alphabetically
  • By standardizing the data format
  • By providing faster access to specific data points (CORRECT)
  • By narrowing down the dataset to only relevant information

Correct! Indexing is a powerful optimization technique that provides faster access to specific data points within a dataset, and this enhances the data analysis process. Quicker access to specific data points allows analysts to navigate datasets more efficiently, thereby facilitating a more in-depth understanding of the data.

3. What does the term “metadata” refer to in the context of Power BI? 

  • It refers to the data that describes and gives information about other data. (CORRECT)
  • It refers to the customized labels and annotations added to enhance data reports.
  • It refers to the highest level of data security.
  • It refers to the summarized version of the data used for visualization.

Correct! Metadata is data about data. It provides information about other data, making it easier to retrieve, manipulate, and manage data. In Power BI, metadata includes information like table names, column names, relationships and data types, as it acts as a map guiding you through the data landscape.

4. What effect does a bi-directional filter have in a many-to-many relationship in Power BI?

  • It automatically reduces the size of the data model.
  • It allows the filter context to flow in either direction. (CORRECT)
  • It prevents any filtering of data.
  • It reduces the complexity of the data model.

Correct! In the context of many-to-many relationships, bi-directional filters allow the filter context to flow in either direction. This means a filter on one table can influence the data shown from the other table, and vice versa.

5. What is the purpose of the Column Quality feature in Power Query Editor in Power BI?

  • It visualizes the frequency and distribution of values within your columns.
  • It provides statistical measures such as count, unique count, average, etc. of your columns.
  • It helps to categorize your data.
  • It allows you to assess the quality of data in your columns by quantifying the percentage of valid, erroneous, or empty entries. (CORRECT)

Correct! The Column Quality feature in Power Query Editor provides a quick overview of potential issues such as erroneous or empty entries in your data. This helps you understand the overall quality of data in your columns.

KNOWLEDGE CHECK: OPTIMIZE DIRECTQUERY MODELS

1. Which of the following statements accurately describes table storage in DirectQuery?

  • Optimizing table storage allows you to import all data in Microsoft Power BI for fast performance.
  • Optimizing table storage allows you to store detailed data for all queries.
  • Optimizing table storage allows you to import selected tables to the memory of Power BI for faster query performance. (CORRECT)

That’s correct! By optimizing table storage, you can select which table needs to be imported and which can be kept in DirectQuery. 

2. Your DirectQuery report is slow in loading and processing data. What technique can you use to enhance report performance?

  • Import entire data into Power BI.
  • Table storage optimization. (CORRECT)
  • Data transformation in the query editor.

That’s correct!  You can optimize the table storage features of Power BI when working with DirectQuery connection to enhance the report performance.

3. True or False: DirectQuery mode automatically optimizes the data model and minimizes the queries sent to the underlying data source.

  • True
  • False (CORRECT)

That’s correct! DirectQuery mode requires manual optimization for improved query performance.

4. You are working in a multi-national retail company who need to build a real-time sales dashboard. The sales transactions are recorded in a centralized SQL database which could contain hundreds of millions of rows. DirectQuery is the best option for the current analysis rather than importing the data. Because you are using DirectQuery, which of the following options should you select in order to optimize the query performance and user experience? Select all that apply.

  • You can create aggregations and aggregated tables. (CORRECT)
  • You can create complex DAX logic within your data model to build user defined measures.
  • You can avoid using too many visuals while creating a report. (CORRECT)
  • You can eliminate unnecessary columns from the data tables. (CORRECT)

That’s correct. Creating aggregations is a feasible option to optimize query performance.

That’s correct! The decrease in the number of visuals in your report also reduced the number of queries sent to the database.

That’s correct! Removing unnecessary rows and fields improve query performance by reducing the data size. 

5. ______________ and __________________ are the two limitations of using DirectQuery in Power BI desktop.

  • Modeling, import
  • Visualization, DAX (CORRECT)
  • Date dimension, network speed

That’s correct! DAX and visualization in reporting are the two major limitations of DirectQuery.

6. Which of the following statements is true regarding data transformation in DirectQuery?

  • Data transformation can be applied to the data in Power BI before sending queries to the data source.
  • Data transformation can only be applied to import mode. 
  • Data transformation is not fully supported in DirectQuery mode. (CORRECT)

Correct! In data that is sourced via DirectQuery, you can apply some transformation operations but not all as you typically do in import mode

7. Why is it important to optimize queries when connecting to the data source via DirectQuery in Power BI? 

  • To improve query performance, response time, and visual refresh (CORRECT)
  • To reduce the number of data sources used in DirectQuery mode.
  • To ensure real-time connectivity is established

Correct! Optimizing queries enables you to improve query performance and response time and refresh time of visuals in the report. 

8. In DirectQuery sourced data, when is it suitable to use dual storage mode in Power BI?

  • When performing performance optimization via query reduction option. 
  • When creating aggregated tables and import to Power BI memory. (CORRECT)
  • When importing the entire dataset to the memory engine of Power BI

Correct! When you create aggregated table in power query and import to in-memory, you need to establish relationship between imported aggregated table and other tables of data model. This requires the storage of mode of shared dimension table between imported tables and DirectQuery sourced table to be dual storage mode. 

SELF-REVIEW: ADDING AN AGGREGATION

1. What is the primary benefit of creating aggregations when working with large datasets in Microsoft Power BI?

  • Faster query execution. (CORRECT)
  • Enhanced visualizations.
  • Real-time data streaming.

That’s correct! Aggregations reduce the size of the dataset imported to Power BI memory, and all queries are sent to in-memory aggregations to speed up the query execution.

2. What is the total number of rows in the original Sales table and the SalesAgg aggregated table respectively?

  • 990 and 999.
  • 57851 and 999.
  • 57851 and 990. (CORRECT)

That’s correct. That is the correct number of rows for both tables.

3. Why is it important to duplicate the source table before creating aggregated tables in DirectQuery connectivity?

  • Duplicating the source table preserves the original table for other analytical needs.
  • Duplicating the source table allows for faster data import.
  • Duplicating the source table improves the quality of the aggregated data. (CORRECT)

That’s correct! Duplicating the original fact table provides the flexibility to utilize the source table for any other analytical needs or for creating other aggregations.

4. True or False: You can create aggregations by defining SQL statement in the source data and then import the table to Power BI. 

  • True (CORRECT)
  • False

Correct! Aggregated tables can be created in SQL and imported to Power BI if you have access to the data source. 

5. True or False: When creating an aggregation, you must reference the data model’s fact table to keep the original table intact.

  • True (CORRECT)
  • False

Correct! To keep the original table intact, you must reference the fact table using the Reference feature from the Queries pane drop-down list.

KNOWLEDGE CHECK: CREATE AND MANAGE AGGREGATIONS

1. Which feature stores pre-aggregated data for improved query performance when Microsoft Power BI is connected to DirectQuery?

  • Aggregated table. (CORRECT)
  • DAX measure.
  • Calculated column.

That’s correct! You need to create aggregated tables when working with DirectQuery connection to store pre-calculated values in the memory of Power BI to enhance query performance. 

2. When connecting to DirectQuery in Microsoft Power BI, which of the following can be used to create aggregated tables? Select all that apply.

  • Data Analysis Expressions
  • SQL queries (CORRECT)
  • Using the Model view of Power BI.
  • Power query editor (CORRECT)

That’s correct! If you have the proper permission to access the database, you can create aggregated tables in the underlying database using SQL queries.

That’s correct! If you do not have access to the underlying database, Microsoft Power BI’s Power Query editor provides you with the necessary tools to create aggregated tables.

3. You have created an aggregation from a fact table connected to Microsoft Power BI via DirectQuery. With the aggregated table created, you then configured the storage mode of the table and established relationships with the other tables of data model. Which of the following steps must be completed before utilizing the aggregation in your reports? 

  • Managing aggregation in Power BI. (CORRECT)
  • Performing transformation operations on the aggregation.
  • Creating calculations on the aggregation.

That’s correct! After creating aggregations, it is essential to manage and configure aggregations in Power BI before you can use them in your reports and visualizations.

4. Which of the following is the recommended approach while selecting the columns for aggregated tables? 

  • Include all columns from the source fact table. 
  • Include only primary key columns.
  • Include columns that fulfill the level of granularity needed for analysis. (CORRECT)

That’s correct! First, you need to decide at which granularity level you want to analyze data. Then, you can select only those columns for aggregation. For example, if you want to analyze sales data based on year and then on product subcategory, you need to select the Sales column along with the Date and Subcategorycolumns to create an aggregation.

5. You have a Microsoft Power BI report with a DirectQuery connection to a large Sales dataset. Business users frequently query total sales and quantities by various categories and months. Which option offers the best approach for optimizing performance while addressing user needs?

  • Import the entire sales table to Power BI memory.
  • Create an aggregated table for total sales and quantities grouped by category and date. (CORRECT)
  • Create an aggregated table for total sales and categories grouped by quantity and date.

That’s correct! Creating an aggregated table by selecting only the Sales amount and Quantities as aggregate columns and Category and Date as group by columns will solve the issue.

MODULE QUIZ: OPTIMIZE A MODEL FOR PERFORMANCE IN POWER BI

1. How does performance optimization in Power BI influence decision-making?

  • It introduces new ways to present data.
  • It facilitates more timely and informed decisions due to faster data analysis. (CORRECT)
  • It ensures that reports are visually appealing while maintaining data integrity.
  • It reduces the number of decisions to be made.

Correct! When Power BI is optimized, it can rapidly analyze and display data, giving users quicker access to insights. This rapid turnaround time means that decision-makers can react promptly to emerging trends, ensuring their choices are both timely and informed.

2. Sales teams at Adventure Works have been entering date information into the system using various formats. This inconsistency has caused issues in past reports, leading to incorrect analyses. The management wants to ensure all dates are standardized for a comprehensive year-end review, and they’ve turned to you, the data analyst for a solution. Which optimization technique should you use?

  • Data transformation. (CORRECT)
  • Indexing by date.
  • Filtering out older dates.
  • Sorting by date.

Correct! The key benefit of data transformation is improving data consistency, which facilitates more accurate and meaningful analyses. It also helps eliminate potential errors in the analysis due to inconsistent data.

3. Which of the following descriptions best describes cardinality in the context of Power BI?

  • The relationship between two tables in a data model.
  • The method used to transform data in Power BI.
  • The sequence in which data is loaded into Power BI.
  • The number of distinct values in a column. (CORRECT)

Correct! Cardinality in Power BI and data modeling signifies the uniqueness of values within a specific column or set. A high cardinality implies many distinct values in a column, whereas a low cardinality suggests repetitive and fewer unique values.

4. What is the potential downside of using bi-directional filters when managing large data volumes in Power BI?

  • They might lead to ambiguous relationships in complex models.
  • They can be resource-intensive, impacting performance. (CORRECT)
  • They limit the number of tables that can be imported.

Correct! Bi-directional filters can increase the complexity of a data model, especially with large data volumes, leading to performance issues. As filter context moves in both directions, it may require recalculations and refinements, leading to performance inefficiencies, if not optimally configured.

5. Your organization has data stored on-premises SQL Server and in cloud-based Azure SQL databases. You want to implement aggregations in Power BI to improve query performance in DirectQuery mode. What do you need to consider in terms of data sources?

  • You can only use cloud-based Azure SQL database as data source for aggregations.
  • You can use both on-premises and cloud databases as data sources for aggregations. (CORRECT)
  • You can only use the on-premises SQL Server database as data source for aggregations.

That’s correct! Aggregations in DirectQuery can be created with any data source supported by DirectQuery, including on-premises and cloud-based databases.

6. True or False: For real-time data analysis and connectivity to an on-premises SQL Server, DirectQuery mode without aggregation is more suitable for improved query performance.

  • True
  • False (CORRECT)

That’s correct! DirectQuery mode, along with aggregations, is advantageous for both real-time access and improved query performance.

7. True or False: When working on a Power BI report connected to a DirectQuery sourced Fact table, you cannot create complex logic in DAX.

  • True  (CORRECT)
  • False 

8. True or False: In DirectQuery sourced Power BI report, the data is stored in the source server. You can import selected or entire data to Power BI memory.

  • True (CORRECT)
  • False

That’s correct! You can always import one or all tables of your dataset that are originally sourced through DirectQuery to the Power BI memory by configuring the storage mode of Power BI.

9. In the context of DirectQuery performance optimization, which of the following are the characteristics of the optimized data model? 

  • A model with only the columns and fields required for analysis. (CORRECT)
  • A model with a list of custom measures to answer analytical questions.
  • A model that contains fewer relationships.

That’s correct! Eliminating unnecessary columns and fields wherever possible can enhance query optimization by reducing the number of queries sent to the underlying data source.

10. Which techniques might you employ to optimize data performance in Power BI? Select all that apply.

  • Adding borders around data points to distinguish them for faster decision-making.
  • Applying sorting to data to group it alphabetically and make it easier to read and interpret. (CORRECT)
  • Filtering data to focus on specific regions or details, removing unnecessary information. (CORRECT)
  • Changing the font style of data points for a faster performance rendering.

Correct! By grouping data alphabetically or numerically, sorting provides you with a more intuitive way to navigate the dataset. This reduction in load speeds up data interpretation ensures data consistency and facilitates faster and more efficient data processing.

Correct! Filtering plays a critical role in data optimization. You’re eliminating unnecessary data points by narrowing down data to a specific set of criteria or parameters. This makes the dataset more streamlined and focused, making it easier to work with.

11. Which of the following is a recommended strategy for managing high cardinality?

  • Expanding data columns to include more details.
  • Frequently changing the data type of columns.
  • Summarization of data during transformation. (CORRECT)

Correct! Summarizing data during transformation is a key strategy to address the performance issues posed by high cardinality. By converting detailed data, like precise timestamps, to broader categories, such as hours or days, cardinality is reduced, enhancing dataset performance and clarity.

12. Which of the following components of Power BI is responsible for managing aggregations? Select all that apply.

  • Power Query
  • The Model view (CORRECT)
  • The Data Pane (CORRECT)
  • Underlying data sources.

That’s correct! After creating aggregations either in SQL Server or in Power query editor, you need to manage aggregations in the data pane or from the model view.

That’s correct! After creating aggregations either in SQL Server or in Power query editor, you need to manage aggregations in the data pane or from the model view.

13. The performance of DirectQuery connection in Power BI depends on which of the following? Select all that apply.

  • Power BI’s memory limit.
  • The size of the dataset. (CORRECT)
  • The server configuration. (CORRECT)
  • The data model relationships.

That’s correct! The size of the dataset is one of the many factors that determine the performance of DirectQuery in Power BI.

That’s correct! The specification of the server on which the database is hosted is also a crucial factor governing the performance of DirectQuery in Power BI.

14. In DirectQuery connectivity, where is the data stored? 

  • In the data source storage (CORRECT)
  • In Power BI cloud storage.
  • In Power BI memory engine.

That’s correct! In DirectQuery connection, data remains in the data source storage. For example, in SQL Server Power BI only retrieves it as needed.

15. Which of the following options can you configure in the Power BI desktop interface to reduce the number of queries sent to the database in DirectQuery mode? Select all that apply.

  • Configure table storage (CORRECT)
  • Configure query reduction option (CORRECT)
  • Configure Server specification
  • Configure SQL queries

That’s correct! You can configure table storage of DirectQuery sourced data in Power BI desktop to import the data table for improved query performance selectively.

That’s correct! Power BI’s query reduction settings provide an efficient way to reduce the number of queries sent to the database by configuring the slicer and filter settings.

16. Which of the following are benefits provided by performance optimization in Power BI? Select all that apply.

  • Improved user experience with smooth and swift report loading. (CORRECT)
  • Enhanced speed and efficiency in executing queries. (CORRECT)
  • Reduces the need to clean and pre-process data before importing to Power BI.
  • More informed and timely decision-making. (CORRECT)

Correct! An optimized Power BI experience translates to minimal lags and swift report rendering. This ensures that users can seamlessly interact with their data, drawing insights without the frustration of long loading times. Such a smooth experience increases user engagement and satisfaction.

Correct! Performance optimization plays a pivotal role in improving query execution. Power BI can retrieve and display data much more rapidly by refining and optimizing data processing. This cuts down waiting time, ensuring that users don’t experience delays, especially when working with extensive datasets.

Correct! In the business world, time is of the essence. With optimized performance, Power BI loads and analyzes data more rapidly, allowing users to discern trends and promptly make decisions. This timeliness ensures that decisions are based on the most recent and relevant data, providing a competitive edge.

17. You need a solution to pinpoint the required information rapidly in your datasets. In Power BI, which optimization technique should you use to accomplish this?

  • Indexing (CORRECT)
  • Filtering
  • Sorting
  • Data visualization

Correct! Indexing allows you to quickly locate data without sifting through the entire dataset. In the context of Power BI, indexing accelerates data retrieval and drastically improves query performance, making the data analysis process smoother and more efficient.

18. Why is cross-filter direction important in Power BI relationships?

  • It influences the sort order of data in a table.
  • It impacts the data refresh intervals.
  • It defines how filters are applied across related tables. (CORRECT)

Correct! The cross-filter direction is important in relationships between two tables because it defines how Power BI applies filters. Cross-filtering has two directions: Single (one-way) and Both (two-way or bi-directional).

19. In DirectQuery connectivity which type of data source is typically used to create aggregations?

  • Only databases like SQL, Azure SQL.
  • All types of flat files (CSV).
  • Any data source supported by DirectQuery. (CORRECT)

20. You are designing a Power BI solution for clients needing real-time access to their on-premises SQL Server database. Which connection mode should you choose?

  • Mix mode
  • Import mode
  • DirectQuery mode (CORRECT)

That’s correct! DirectQuery mode allows you to retrieve real-time data while creating aggregations enhancing the query performance.

21. You’ve identified a need for performance optimization in Power BI. Why is this optimization crucial when dealing with vast data sets?

  • It streamlines data storage without impacting performance.
  • It ensures that reports and dashboards run as smoothly and quickly as possible even as data grows. (CORRECT)
  • It allows more customization options in the Power BI dashboard.
  • It enhances Power BI’s ability to work alongside other tools.

Correct! As businesses grow, so does the volume of their data. Optimization ensures that this increase in data doesn’t degrade the performance of Power BI reports and dashboards. The primary goal is to maintain a consistent user experience, regardless of how large the underlying dataset becomes.

22. Why might using bi-directional filters in Power BI become resource-intensive in certain situations?

  • They occasionally trigger unnecessary calculations in DAX formulas.
  • They make simultaneous connections to all related tables.
  • They allow the filter context to flow in both directions, complicating the data model. (CORRECT)

Correct! Bi-directional filters can increase the complexity of a data model, especially with large data volumes, leading to performance issues. As filter context moves in both directions, it may require recalculations and refinements, leading to performance inefficiencies, if not optimally configured.

23. You are addressing a client’s requirement for real-time access to an on-premises SQL Server and optimizing query efficiency. Which of the following actions must you perform to achieve this task? Select all that apply.

  • Query the original data source for all analytical needs.
  • Connect Power BI via DirectQuery mode. (CORRECT)
  • Import the data to Power BI memory.
  • Create aggregations based on DirectQuery sourced table. (CORRECT)

That’s correct! Creating a DirectQuery connection allows you to access real-time data from SQL Server data sources.

That’s correct! Creating aggregations on DirectQuery sourced tables improves the performance and efficiency of query.

24. Which of the following best describes the limitation of DirectQuery connectivity in Power BI?

  • High memory consumption.
  • Limited data transformation in Power Query editor. (CORRECT)
  • Inability to create relationships between the data tables.

That’s correct! In DirectQuery connectivity, you can perform limited data transformation in the query editor. However, not all transformations are supported.

25. True or False: Limiting the number of visuals and filters in Power BI report helps you optimize the report performance when connected to DirectQuery sourced datasets.

  • True (CORRECT)
  • False

That’s correct! You can reduce the number of visuals on the report page or the number of fields used in a visual. This helps reduce the number of queries sent to the underlying dataset and to optimize overall report performance.

26. You have created an aggregation from a DirectQuery-sourced sales table, but you cannot use the aggregation in Power BI. Which of the following steps are missing before utilizing aggregations in your reports? Select all that apply.

  • Configure the storage mode of the aggregated table. (CORRECT)
  • Manage your aggregations in Power BI desktop. (CORRECT)
  • Data transformation in the query editor.
  • Build the required relationships in the Model view. (CORRECT)

That’s correct! Changing the storage mode of aggregated table to import is necessary to utilize aggregations in your reports and visualization. 

That’s correct! Aggregations must be manged and configured before they can be used in reports and visualizations.

That’s correct! Creating a relationship between aggregated table and existing dimension tables in the data model is mandatory to produce accurate integrated results.

27. Why is high cardinality a possible concern for data analysts?

  • It presents data in the finest granularity.
  • It can slow down the performance of Power BI reports. (CORRECT)
  • It reduces the size of the data model.
  • It creates more direct relationships between tables in the data model.

Correct! High cardinality can increase the size of your data model and the time taken to process queries, slowing down your Power BI reports. While high cardinality can provide rich, detailed data, it comes with the trade-off of potentially reduced performance.

28. You are developing a Power BI solution for a client who requires real-time analysis based on a cloud-based database. While designing the solution you need to connect Power BI to the database via DirectQuery. Which of the following could be a constraint in developing this solution? Select all that apply.

  • Slower query performance compared to import mode. (CORRECT)
  • An inability to perform complex custom measures using DAX expressions. (CORRECT)
  • A limited ability to establish relationships between the data tables.
  • A reduced ability to perform complex data transformation within Power BI. (CORRECT)

That’s correct! It is always faster to query data from memory of Power BI rather than querying it from the data source which depends on numerous factors, like size, server configuration and network speed.

That’s correct! DirectQuery does not support performing complex custom calculations using DAX expressions.

That’s correct! You cannot perform many transformations operation that you normally do in import mode, when connected to DirectQuery in Power BI.

29. On creating an aggregated table from a DirectQuery sourced table, you need to configure the storage mode of aggregated table. What are the remaining two steps that you need to accomplish before using the aggregation in your reports?

  • Manage relationships and create DAX measures.
  • Perform transformations on aggregated table and build relationships.
  • Build relationships and manage aggregations. (CORRECT)

That’s correct! Mountain bikes is the subcategory of product, and the United Kingdom is a specific region, both introduce filters which make use of CALCULATE functions necessary to get accurate results.

CONCLUSION – Optimize a model for performance in Power BI

In conclusion, this module offers a comprehensive exploration of the optimization process in Power BI, highlighting essential tools and methods such as performance analyzer and DirectQuery. By providing a deep dive into the practical applications of Data Analysis Expressions (DAX) in real-world scenarios, the module equips learners with the necessary skills to enhance Power BI performance and effectively utilize DAX for advanced data analysis. Through this integrated approach, learners gain the expertise needed to optimize their use of Power BI and drive more efficient and insightful business intelligence outcomes.

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