Chapter 1 – Introduction to Power BI Data Cleaning

1. B – 50-80%
2. D – Power Query, data modeling, DAX formulas
3. C – Data transformation and preparation
4. C – As a formula language for creating calculations and measures
5. B – To bridge the gap between relational databases and spreadsheet tools
6. D – It can be used for both calculations and querying within Power BI
7. B – It enhances clarity and reduces ambiguity

Chapter 2 – Understanding Data Quality and Why Data Cleaning is Important

1. A – The extent to which data represents true values and attributes
2. D – Human errors during data entry
3. B – Data completeness
4. B – It helps maintain data integrity and accuracy
5. B – A culture of data stewardship
6. A – Proactively identifying and addressing data quality issues
7. C – Criteria for data accuracy, completeness, consistency, validity, and timeliness
8. A – Minimizing human errors
9. B – They automate the data cleaning process

Chapter 3 – Data Cleaning Fundamentals and Principles

1. B – Transforming data into a masterpiece – The aim of data cleaning in the data preparation process is to refine and enhance raw data, ensuring it is accurate, consistent, and high-quality for effective analysis
2. B – To prevent a cycle of perpetual data cleaning – While the other answers may have some truth to them, they do not describe why it is essential to establish a framework and principles for data cleaning efforts
3. B – Data assessment, data profiling, data validation, data cleaning strategies, data transformation, data quality assurance, and documentation– These processes together are involved in the data cleaning process
4. A – Patterns, distributions, and outliers – Data profiling aids in recognizing patterns, understanding distributions, and identifying outliers, providing crucial insights for effective data cleaning and quality improvement
...

Chapter 4 – The Most Common Data Cleaning Operations

1. B – To enhance data accuracy in the analysis – Removing duplicates is crucial to prevent inaccuracies in data analysis, especially when dealing with numerical values.
2. C – Product Name, as the main identifier – In the provided example, the Product Name column is selected to remove duplicates, as it serves as the main identifier.
3. B – Distorts analysis results – Missing data, or NULL values, can distort analysis results and visuals.
4. C – To gain desired dimensions for analysis – for example, splitting a date field – Columns may need to be split to extract specific dimensions for analysis.
5. C – Split Columns by Delimiter, based on data format – In the Date table example, the By Delimiter function is used to split the date column based on the / delimiter.
6. C – Merging columns to format date data – Merging columns may...

Chapter 5 – Importing Data into Power BI

1. C – Data completeness – Ensuring data completeness is essential for accurate analyses and reliable reporting in Power BI. By using data profiling techniques, users can identify columns with high percentages of missing values, such as the ProductSize column in the provided example. This allows for targeted attention to areas requiring data completion.
2. C – Conditional formatting – Conditional formatting in Power BI is a valuable tool for validating data accuracy. Users can define rules to highlight data points falling outside predefined accuracy ranges. This method, as showcased in this chapter, ensures that potential errors or outliers are flagged for further investigation, promoting trustworthy insights.
3. D – Calculated columns and measures – Power BI’s DAX language empowers users to create calculated columns and measures, enforcing consistent data rules and business logic....

Chapter 6 – Cleaning Data with Query Editor

1. C – Power Query ribbon – The crucial components of the Query Editor interface include the Power Query ribbon, navigation pane, preview pane, and settings pane. However, the correct answer in the multiple-choice format is Preview Binoculars.
2. C – Translating high-level transformations into low-level SQL statements – Query folding is the process of translating high-level transformations into low-level SQL statements, optimizing query execution.
3. C – Adding columns – The technique that allows the creation of new data based on existing columns is adding calculated columns.
4. B – Join types – The factor that determines how records are matched between tables in merging queries is join types.
5. C – Loading, cleaning, and shaping data – Power Query is used for loading, cleaning, and shaping data.

Chapter 7 – Transforming Data with the M Language

1. B – Transforming entire columns or tables – M’s purpose is transforming entire columns or tables
2. C – let – The keyword marking the beginning of an M variable declaration block is let
3. C – Using a variable, often named Source – A data source is typically connected using the Source function
4. B – A step/identifier that includes a space or special characters – The # symbol helps to identify steps or identifiers that include spaces or special characters within the name
5. B – Number.From – The function used to convert extracted text into a numeric value is Number.From

Chapter 8 – Using Data Profiling for Exploratory Data Analysis (EDA)

1. B – To summarize data characteristics and gain insights – EDA serves as a pivotal phase in the data analysis workflow, aiming to summarize data characteristics, identify patterns, detect outliers, and gain insights into data structure.
2. A – Identifying potential outliers – Benefits of a well-carried-out EDA include familiarizing analysts with the dataset, assessing data scope, identifying data quality issues, revealing patterns and trends, and aiding in the selection of appropriate modeling techniques.
3. C – Column Transformation – Power BI’s data profiling capabilities include the following:
   • Column Quality Assessment
   • Column Distribution Analysis
   • Column Profile Views
4. B – Within Power Query, open the View tab – Data profile views can be accessed in Power Query by opening Power Query and selecting the View tab.
5. B – Histograms...

Chapter 9 – Advanced Data Cleaning Techniques

1. C – Fuzzy matching and fill down – They are the two essential techniques discussed in the chapter for cleaning and preparing data using the Query Editor in Power BI
2. C – Range from 0 to 1, indicating no to perfect similarity – In the context of fuzzy matching, the similarity score ranges from 0 to 1, indicating no to perfect similarity
3. D – When working with time series data and maintaining data continuity – The fill down technique in Power BI’s Query Editor is particularly useful in this scenario
4. D – Regularly validate the results of data cleaning efforts and maintain documentation – This is a crucial best practice emphasized when working with fuzzy matching and fill down in Power BI
5. C – To extend the capabilities of Power BI by leveraging external ecosystems – This is the primary purpose of using custom data scripts in languages such...

Chapter 10 – Creating Custom Functions in Power Query

1. C – Defining the problem – The first step in planning for a custom function is to clearly define the problem that the function will solve.
2. C – Making functions flexible and adaptable – Parameters in custom functions allow flexibility by serving as variables that users can adjust, making the function applicable to various scenarios.
3. C – To improve the overall user experience – Default parameter values enhance user friendliness, allowing users to quickly understand and use the function without extensive configuration.
4. C – Choosing a descriptive name – Choosing a descriptive name is crucial for the structure of a custom function as it provides clarity about the function’s purpose and use.

Chapter 11 – M Query Optimization

1. B – Filtering and reducing data, using native M functions, creating custom functions, optimizing memory usage – These are the four key tips to optimizing M queries
2. A – Parameters: table, weights, values; the weighted average is calculated by summing the weighted values and dividing by the total weight – The function takes three parameters (table, weights, values) and calculates the weighted average by summing the weighted values and dividing by the total weight.
3. C – It loads a table into memory once, reducing memory duplication – Table.Buffer is used to load a table into memory only once, reducing memory duplication and improving query speeds on subsequent steps. Note though that it can also have the reverse effect as the initial reading and loading of the data can cause your query to run more slowly.
4. B – Splits a table into smaller partitions for parallel processing –...

Chapter 12 – Data Modeling and Managing Relationships

1. C – Data modeling – The primary focus of managing relationships in Power BI is data modeling. This involves structuring data tables, creating relationships between them, and ensuring a proper foundation for analysis.
2. B – It can enhance capabilities but may lead to errors and performance issues – Bidirectional cross-filtering (BDCF) is considered a double-edged sword because while it can enhance analytical capabilities, it may introduce errors and performance issues if not used carefully.
3. B – A feature allowing tables to filter each other in both directions – Bidirectional cross-filtering is a feature in Power BI that allows tables to filter each other in both directions, providing more flexibility in data analysis.
4. B – It defines the nature of relationships between tables – Understanding cardinality is crucial in Power BI data modeling as it defines...

Chapter 13 – Preparing Data for Paginated Reporting

1. B – Pixel-perfect, highly formatted reporting – Power BI Report Builder is designed for creating paginated reports that are highly formatted, pixel-perfect, and optimized for printing or generating PDFs
2. B – Structuring and organizing data – Row groups and column groups in paginated reports play a crucial role in organizing and structuring data, creating hierarchical structures, and facilitating aggregated analysis
3. B – To enhance user experience and efficiency – Filters and parameters are important in paginated reporting to enhance user experience and efficiency by providing dynamic interactivity and customization options
4. D – By generating paginated reports with precise formatting – Power BI Report Builder contributes to meeting compliance standards by allowing the creation of paginated reports with precise formatting, which is crucial in industries with...

Chapter 14 – Automating Data Cleaning Tasks with Power Automate

1. B – Workflow automation – This is the primary purpose of Power Automate in conjunction with Power BI
2. C – Weather change – This is NOT a trigger mentioned in the chapter
3. B – To communicate workflow failures and successes – The notifications in Power Automate help in achieving this
4. B – Set up a recurrence action – Although you can use a manual trigger to refresh data, we set up a recurrence action in order to schedule the data refreshes

Chapter 15 – Making Life Easier with OpenAI

1. B – Cleaning textual data, identifying anomalies and outliers, and data imputation strategies – Azure OpenAI can assist in cleaning textual data, identifying anomalies and outliers, and implementing data imputation strategies
2. C – A filtering step before a group by transformation – The example conversation shows that to filter out products with sales less than $1,000, a filtering step is added before the group by transformation.
3. D – Adapting quickly to shifting requirements – Data cleaning and optimization requirements are often dynamic, and AI models may struggle to adapt quickly to shifting requirements
4. A – Over-reliance on AI recommendations – The chapter warned about the potential pitfall of over-reliance on AI recommendations without critical scrutiny, which may lead to suboptimal transformations