The Foundation of a Data Analyst Career with PL-300
The Microsoft Power BI Data Analyst Certification represents a significant professional milestone for individuals dedicated to the field of data analysis and business intelligence. This credential formally validates a candidate's proficiency in utilizing the Microsoft Power BI suite of tools to transform raw data into actionable insights. The certification journey, culminating in the PL-300 exam, is designed to cover the full spectrum of a data analyst's responsibilities. It encompasses everything from connecting to and transforming data from various sources to modeling that data for performance and scalability, and finally, visualizing it through compelling reports and dashboards.
This certification is more than just a test of technical knowledge; it is a testament to one's ability to think critically about data and its role in an organization. It proves that a professional can work with business stakeholders to understand their requirements, translate those needs into technical specifications, and deliver data-driven solutions that support strategic decision-making. In a world where data is a critical corporate asset, certified professionals are recognized for their skills in maximizing the value of this asset, ensuring its integrity, and presenting it in a way that is accessible and understandable to a non-technical audience.
The PL-300 exam has evolved from its predecessor, the DA-100, refining its focus to align with the current demands of the industry and the expanding capabilities of the Power BI platform. While the core principles remain the same, the updated certification places a strong emphasis on a comprehensive skill set that enables analysts to build robust, efficient, and impactful business intelligence solutions. Achieving this certification signals to the market that you are not just a user of Power BI, but a skilled professional capable of leveraging its full potential to drive business value and foster a data-centric culture within any organization.
The Growing Demand for Power BI Professionals
In today's economy, the ability to analyze vast quantities of data is no longer a niche skill but a fundamental business requirement. Companies across all sectors are actively seeking professionals who can help them make sense of their operational, financial, and customer data. The Power BI platform has emerged as a leader in the business intelligence space, making skills related to it exceptionally valuable. The demand for proficient Power BI data analysts is not merely a passing trend; it reflects a fundamental shift in how businesses operate, with a growing reliance on data to guide every decision.
This surge in demand is evident in market trends and job postings. Organizations are looking for individuals who can build interactive dashboards that provide real-time insights, create scalable data models that serve as a single source of truth, and implement security protocols to govern data access. A Power BI professional helps bridge the gap between complex datasets and clear, actionable business strategies. They empower leadership teams to move beyond intuition and base their decisions on empirical evidence, leading to improved efficiency, identified growth opportunities, and a significant competitive advantage in the marketplace.
The financial incentive for acquiring these skills is also compelling. Industry reports and salary surveys consistently show that professionals with Microsoft certifications, particularly in high-demand areas like data analytics, often command higher salaries. Earning the Power BI Data Analyst Associate certification can lead to significant remuneration increases and opens doors to more senior roles. It serves as a clear differentiator in a competitive job market, proving to potential employers that you have the validated expertise to make an immediate and meaningful impact on their data initiatives.
Core Competencies Developed Through the PL-300 Journey
The PL-300 certification curriculum is meticulously structured to build a comprehensive and practical skill set. It goes far beyond simply teaching you how to create charts. A primary focus is on data modeling, which is the foundational element of any robust Power BI solution. You will learn how to connect to disparate data sources, establish well-defined relationships between tables, and structure the data in a way that is optimized for performance and ease of use. This includes understanding concepts like star schemas and the importance of creating clean, logical models that can scale with the business.
Another critical area of learning is data visualization and report design. The certification teaches you the art and science of presenting data effectively. This involves selecting the appropriate visual for the insight you want to convey, formatting reports for clarity and impact, and enabling user interactivity to allow for deeper exploration of the data. You will learn how to build a narrative with your data, guiding users through a logical story that highlights key trends, patterns, and outliers. The goal is to create reports that are not only informative but also engaging and intuitive for the end-user.
Furthermore, the PL-300 certification ensures you master the analytical capabilities within Power BI. This involves a deep dive into Data Analysis Expressions (DAX), the formula language used to create custom calculations. You will learn to write sophisticated measures and calculated columns that add new layers of insight to your data model, enabling complex analyses that would be impossible with the raw data alone. From simple aggregations to complex time intelligence functions, mastering DAX is a key component of what separates a novice user from a certified Power BI professional.
Who is the Ideal Candidate for This Certification?
The PL-300 certification is designed for a broad audience of professionals who work with data in various capacities. Its primary target is individuals who hold or aspire to the role of a data analyst or business intelligence developer. These are the people whose main responsibility is to dive deep into datasets, clean and transform the information, and build the reports and dashboards that the rest of the organization will consume. They are expected to have a foundational understanding of data concepts and are looking to formalize their skills with a globally recognized credential.
However, the applicability of this certification extends beyond dedicated analyst roles. Business users who are heavily reliant on data to perform their jobs can also benefit immensely. This includes professionals in finance, marketing, sales, and operations who want to enhance their ability to self-serve their analytical needs. By learning Power BI through the PL-300 framework, they can reduce their dependency on IT departments, build their own reports, and gain faster access to the insights they need to succeed in their roles, fostering a more agile and data-literate business environment.
Additionally, aspiring data professionals and recent graduates can use the PL-300 certification as a powerful launchpad for their careers. In a competitive entry-level job market, this certification provides tangible proof of practical skills and a commitment to the data analytics field. It demonstrates a proactive approach to learning and a solid understanding of one of the leading tools in the industry. For anyone looking to enter or advance within the world of data, the Power BI Data Analyst certification provides a clear and structured path to acquiring the essential skills needed for success.
The Strategic Business Value of a Certified Analyst
A certified Power BI Data Analyst brings immense strategic value to an organization. Their primary function is to deliver actionable insights by leveraging available data and applying their subject matter expertise. They act as a crucial link between the technical data infrastructure and the business units that rely on information for daily operations and strategic planning. A certified analyst is skilled in collaborating with key stakeholders across different departments to accurately identify and define business requirements, ensuring that the resulting solutions are perfectly aligned with organizational goals.
One of the most critical contributions of these professionals is their ability to cleanse and transform raw data. Data in its original form is often messy, incomplete, and inconsistent. A certified analyst uses tools like Power Query to perform the essential tasks of data preparation, creating a clean, reliable, and trustworthy dataset. This foundational work is paramount, as the quality of any analysis or report is directly dependent on the quality of the underlying data. By ensuring data integrity, they build a solid base for all mission-critical, data-driven decisions within the company.
Ultimately, the goal is to create meaningful business value through data visualization. A certified analyst excels at designing and developing data models and then building reports that are clear, concise, and easy to interpret. They use visual storytelling techniques to highlight important trends and provide context, transforming complex data into understandable information that empowers leaders to make informed choices. They also play a key role in enabling a culture of self-service analytics, empowering their colleagues to perform their own analysis efficiently and effectively, thereby scaling the impact of data across the entire organization.
Navigating the PL-300 Exam Structure
Understanding the structure of the PL-300 exam is the first step toward successful preparation. The exam is a comprehensive assessment that typically consists of 40 to 60 questions, which must be completed within a set time frame. The questions are not limited to a single format; candidates can expect a variety of question types, including multiple-choice, drag-and-drop, case studies, and active screen scenarios. This mixed format is designed to test not only theoretical knowledge but also the practical ability to apply concepts in real-world situations, making it a robust evaluation of a candidate's skills.
The case study sections are a particularly important component of the exam. In these scenarios, you are presented with a detailed business problem, complete with sample data, business requirements, and technical constraints. You will then need to answer a series of questions related to that case study. This format tests your ability to comprehend a complex situation, analyze the requirements, and determine the best course of action. It closely mimics the problem-solving process that data analysts undertake in their day-to-day work, requiring a holistic understanding of the subject matter.
Another key aspect to be aware of is that the exam is not static. Microsoft periodically updates the content to reflect the latest features and best practices within the Power BI service. Therefore, it is crucial for candidates to refer to the official exam skills outline provided by Microsoft when preparing. This document details the specific domains and tasks that will be assessed, along with the percentage weightage for each area. Aligning your study plan with this official guide ensures that you are focusing your efforts on the most relevant topics and are fully prepared for the content you will encounter on exam day.
Essential Foundational Knowledge for Success
While the PL-300 exam has no formal mandatory prerequisites, there is a clear set of foundational knowledge that is highly recommended for any candidate aspiring to pass. A strong understanding of data processing fundamentals is paramount. This includes familiarity with core concepts like relational databases, data warehousing, and the differences between various data structures. Candidates should be comfortable with the idea of data repositories and understand the basics of how data is stored and accessed, both in on-premise environments and in cloud-based infrastructures like Azure.
Proficiency with Power Query is non-negotiable for this certification. A significant portion of the exam is dedicated to preparing and transforming data, and Power Query is the primary tool for these tasks within the Power BI ecosystem. You should have hands-on experience using the Power Query Editor to connect to data sources, apply various transformation steps to clean and shape the data, and handle common data quality issues. Understanding how to merge, append, and pivot data are essential skills that will be thoroughly tested.
Equally important is a fundamental grasp of Data Analysis Expressions (DAX). While you are not expected to be a DAX guru at this stage, you must know how to write basic expressions to create calculated columns and measures. Understanding the context of calculations (row context vs. filter context) and being familiar with common functions for aggregation, filtering, and time intelligence will be critical. This foundational knowledge forms the basis for building the robust and insightful data models that the exam requires, making it an essential area of focus in your preparation.
Understanding the Data Preparation Domain
The process of preparing data is the foundational pillar upon which all successful data analysis rests. In the context of the PL-300 exam, this domain holds significant weight, typically accounting for a quarter to nearly a third of the total score. This emphasis underscores the real-world importance of these skills. Before any meaningful visualization or analysis can occur, data must be sourced, cleaned, and reshaped into a usable format. This domain tests your ability to execute these critical tasks efficiently and effectively using the tools available within the Power BI ecosystem, primarily the Power Query Editor.
This section of the exam evaluates a candidate's proficiency in a series of sequential steps. It begins with connecting to a wide array of data sources, from simple flat files to complex relational databases and web-based services. Once connected, the focus shifts to the crucial processes of profiling, cleaning, and transforming the data. This involves identifying and rectifying errors, handling missing values, standardizing formats, and restructuring tables to create a clean and reliable dataset. A deep understanding of these tasks is essential, as they ensure the accuracy and integrity of all subsequent analysis.
Finally, this domain covers the process of loading the prepared data into the Power BI data model. This involves making conscious decisions about which queries to load and which to use only as staging steps, as well as understanding the implications of different data loading settings. Mastering this domain means you are not just capable of importing data, but are skilled in the art and science of data wrangling, transforming raw, often chaotic, information into a pristine asset ready for modeling and visualization. It is a non-negotiable skill set for any aspiring Power BI Data Analyst.
Getting Data from Diverse Data Sources
A core competency for any data analyst is the ability to connect to and ingest data from a multitude of different sources. The PL-300 exam thoroughly assesses this skill, reflecting the heterogeneous data landscapes of modern organizations. You will be expected to demonstrate proficiency in connecting Power BI to a wide variety of data repositories. This includes common file types such as Excel workbooks, comma-separated values (CSV) files, and text files. You must understand the different connection options and potential pitfalls associated with each of these basic file formats.
The scope extends far beyond simple files. A significant focus is placed on connecting to relational databases. You will need to be comfortable connecting to systems like SQL Server, understanding the difference between Import and DirectQuery modes, and knowing how to write or use native SQL queries to retrieve specific subsets of data. The exam will test your ability to handle database credentials securely and to navigate the schemas of these sources to select the appropriate tables and views required for your analysis.
Furthermore, the modern analyst must be adept at sourcing data from cloud-based and web sources. The PL-300 exam covers connecting to online services, which can range from SharePoint lists to cloud databases and other software-as-a-service platforms. You will also need to know how to extract data from web pages, which often involves navigating HTML tables and understanding how to parameterize your connections to handle pagination or other dynamic web content. This breadth of knowledge ensures that a certified analyst can acquire the necessary data, regardless of where it resides.
The Art of Data Cleansing and Transformation
Once data is brought into the Power Query Editor, the real work of preparation begins. This phase is all about cleansing and transforming the data to make it suitable for analysis. The PL-300 exam places a heavy emphasis on your ability to perform these tasks methodically. Data cleansing involves a range of activities aimed at improving data quality. This includes identifying and handling missing values through strategies like removing rows, replacing them with a specific value, or using imputation techniques. It also involves correcting data entry errors and removing duplicate records to ensure data accuracy.
Data transformation involves reshaping the dataset to better suit the analytical model. You will be tested on your ability to perform structural changes to your tables. This includes fundamental operations such as pivoting and unpivoting columns, which are essential for converting data from a crosstab format to a columnar format that is more efficient for analysis. You will also need to master tasks like splitting columns based on delimiters, merging columns to create new composite keys, and applying various text, number, and date transformations to standardize the data.
Each of these transformations is applied as a step within the Power Query Editor, creating a repeatable and editable sequence of actions. A key skill assessed on the exam is the ability to navigate, modify, and organize these applied steps. This includes renaming steps for clarity, reordering them where appropriate, and understanding how the sequence of transformations can impact performance and the final output. This meticulous process ensures that the data loaded into the model is not only clean and accurate but also structured optimally for efficient analysis and reporting.
Profiling and Understanding Your Data
Before you can effectively clean or transform data, you must first understand its characteristics. Data profiling is the critical process of examining your source data to discover its structure, content, and quality. The Power Query Editor provides a suite of powerful data profiling tools, and the PL-300 exam will expect you to be proficient in their use. These tools allow you to quickly gain insights into your dataset without having to write complex queries or manually inspect thousands of rows of data.
The main features for data profiling include Column Quality, Column Distribution, and Column Profile. The Column Quality feature provides a quick overview of each column, showing percentages for valid data, errors, and empty values. This is often the first step in identifying columns that require attention. The Column Distribution feature provides a histogram that visualizes the frequency of values within a column, helping you to spot outliers and understand the spread of your data. These visual cues are invaluable for quickly assessing the health of your dataset.
For a more in-depth look, the Column Profile pane provides detailed statistics about a selected column, including counts of distinct and unique values, min/max values, and a frequency distribution of the most common entries. Using these tools effectively allows you to make informed decisions about your data preparation strategy. By first profiling your data, you can systematically identify issues and formulate a plan to address them, leading to a more efficient and accurate data cleansing process. This analytical approach to data preparation is a key attribute of a skilled data analyst.
Combining Data with Merges and Appends
Rarely does all the data required for an analysis reside in a single table or source. A common task for a data analyst is to combine data from multiple queries. The PL-300 exam will test your ability to perform these operations using the merge and append functionalities in Power Query. While both actions combine data, they do so in fundamentally different ways, and you must understand when to use each one. An append operation is used to stack rows from two or more tables that share the same or similar column structures, effectively adding more rows to your dataset.
Appending is useful when you have data partitioned across multiple files or tables, such as monthly sales data stored in separate files for each month. By appending these queries, you can create a single, comprehensive table containing all the sales data for the entire period. You will need to understand how Power Query handles columns that do not match perfectly between the appended tables and how to ensure the resulting table structure is correct and usable for your analysis.
In contrast, a merge operation is used to join two tables together based on a common column, similar to a JOIN in SQL. This allows you to add new columns to a table, enriching it with data from another source. For example, you could merge a sales transaction table with a product dimension table using a common Product ID column to bring in product details like name, category, and price into the sales table. The exam will require you to be proficient in performing different types of merge joins, such as inner, left outer, and full outer, and to understand the outcome of each join type.
Working with Structured and Unstructured Data
Data analysts must be prepared to work with data in various formats, not all of which are neatly organized into traditional rows and columns. The PL-300 certification assesses your ability to handle both structured and semi-structured data sources. Structured data, like that from relational databases or Excel tables, is straightforward to work with. However, you will also encounter semi-structured data formats like JSON or XML, which are common in web APIs and other modern data sources.
You must demonstrate the ability to parse these hierarchical formats within Power Query. This involves navigating through the nested records and lists to expand them into a flat, tabular structure that can be used in your data model. For example, when connecting to a JSON source, you will need to know how to drill down into the data, expand the relevant lists into new rows, and expand the records into new columns. Understanding this process is crucial for leveraging data from many modern application programming interfaces (APIs).
Beyond these formats, you might also need to extract information from less structured sources. This could involve parsing text columns that contain multiple pieces of information concatenated together. You will be expected to use Power Query's text transformation functions, such as splitting columns by delimiter or extracting text based on patterns, to create structured columns from a single block of text. This skill is vital for unlocking the value hidden within comment fields, log files, or other free-form text data sources, showcasing your versatility as a data analyst.
Implementing Parameters for Dynamic Queries
To create more flexible and manageable Power BI reports, it is often beneficial to parameterize aspects of the data preparation process. The PL-300 exam will test your understanding of how to create and use parameters within Power Query. Parameters allow you to store a value, such as a server name, a file path, or a date, in one place and then reference it in multiple queries. This makes your reports much easier to maintain and update.
For instance, instead of hardcoding a file path into a query that connects to a local file, you can create a parameter for the folder path. If the location of your source files ever changes, you only need to update the value of the parameter, and all the queries that reference it will automatically point to the new location. This is far more efficient than manually editing each individual query. This technique is especially useful when deploying reports from a development environment to a production environment where file paths or server names are different.
Parameters can also be used to make your data retrieval more dynamic. For example, you could create parameters for a start date and an end date. These parameters could then be used to filter the data being pulled from a large database, ensuring that you only load the specific date range required for the analysis. This can dramatically improve the performance of your data refresh by reducing the amount of data that needs to be processed. The ability to effectively implement parameters demonstrates a more advanced understanding of Power Query and contributes to building more robust and maintainable data solutions.
The Core Principles of Data Modeling
Data modeling is arguably the most critical skill for a Power BI data analyst and forms a substantial part of the PL-300 exam. A data model is not just a collection of tables; it is the semantic layer that defines the business logic and relationships, serving as the engine for all reports and visualizations. This exam domain tests your ability to design and implement a data model that is both accurate in its representation of business processes and optimized for performance. It covers everything from designing the schema to writing the DAX calculations that bring the model to life.
A well-designed data model is characterized by its simplicity and efficiency. The exam will expect you to understand and apply best-practice principles, such as the star schema. This design pattern involves organizing your tables into fact tables, which contain transactional data like sales or events, and dimension tables, which contain descriptive attributes like customers, products, or dates. This structure is highly optimized for the VertiPaq engine that powers Power BI, leading to faster report performance and more intuitive analysis for end-users.
Mastering data modeling means understanding the trade-offs between different design choices. You will need to know how to create relationships between tables, set their cardinality and cross-filter direction correctly, and understand the impact these settings have on your calculations. The ultimate goal is to build a model that is easy to understand, provides fast query responses, and can be easily maintained and extended as business requirements evolve. This skill is what elevates a report creator to a true business intelligence professional.
Designing and Implementing a Star Schema
At the heart of effective Power BI data modeling is the star schema. The PL-300 exam requires a thorough understanding of this design methodology. A star schema is a mature modeling approach that organizes data into two primary types of tables: fact tables and dimension tables. Fact tables are located at the center of the schema and store the quantitative, numerical data about a business process. These tables typically contain foreign key columns that relate to the dimension tables, along with numeric measure columns.
Surrounding the fact table are the dimension tables. Each dimension table describes a specific business entity, such as Products, Customers, or Time. They contain the qualitative, descriptive attributes that are used to slice and dice the data in the fact table. For example, a Product dimension table might contain columns for product name, category, color, and size. The relationship between the fact table and the dimension tables is typically a one-to-many relationship, with the "one" side being the dimension table and the "many" side being the fact table.
The exam will assess your ability to identify which tables in a given dataset should be designated as facts and which as dimensions. You will also be tested on your ability to structure these tables correctly, for instance, by ensuring that dimension tables have a unique key for the relationship. The reason the star schema is so heavily emphasized is its performance benefits. The simple, clean relationships and reduced number of joins make it incredibly efficient for the Power BI engine to aggregate and filter data, resulting in a significantly better user experience with faster, more responsive reports.
Creating Relationships and Defining Cardinality
Once your tables are loaded into Power BI, the next crucial step is to define the relationships between them. These relationships are what allow you to filter and analyze data across multiple tables simultaneously. The PL-300 exam will test your ability to create these relationships and, more importantly, to configure them correctly. This involves selecting the correct columns on which to join the tables, which are typically a primary key in the dimension table and a foreign key in the fact table.
A key concept you must master is cardinality. Cardinality refers to the uniqueness of values in a column and defines the nature of the relationship between two tables. The most common cardinality types you will encounter are one-to-many, one-to-one, and many-to-many. The exam will require you to identify the appropriate cardinality for a given scenario. For example, the relationship between a Products dimension table and a Sales fact table is a classic one-to-many, as one product can be involved in many sales transactions.
In addition to cardinality, you must also understand the cross-filter direction of a relationship. This setting determines how filters propagate from one table to another. A single cross-filter direction is the most common and generally the best practice, as it creates unambiguous filter paths. However, there are specific scenarios where a bi-directional filter may be necessary. The exam will test your ability to recognize these scenarios and understand the performance implications of using bi-directional relationships, as they can sometimes lead to ambiguity and slower report performance if not used carefully.
Introduction to Data Analysis Expressions (DAX)
Data Analysis Expressions (DAX) is the formula language used throughout Power BI to create custom calculations on your data model. A solid understanding of DAX is essential for passing the PL-300 exam and for unlocking the true analytical power of Power BI. DAX allows you to go beyond the basic aggregations that are created automatically and build sophisticated calculations that answer specific business questions. The language is a collection of functions, operators, and constants that can be used in a formula to calculate and return one or more values.
The exam focuses on your ability to create two main types of DAX calculations: calculated columns and measures. A calculated column is a new column that you add to a table in your model. The value for each row in this column is computed based on a DAX formula and is calculated during the data refresh process. In contrast, a measure is a calculation that is performed at query time, in response to user interactions with a report. Measures do not store their results in the model, making them ideal for aggregating data.
Understanding the difference between calculated columns and measures is a fundamental concept that will be tested. Generally, calculated columns are best for static values that you want to use for slicing or filtering, while measures are used for dynamic aggregations like sums, averages, or counts that change based on the filter context applied in a report. Your ability to write effective DAX code, starting with simple aggregations and moving to more complex formulas, is a cornerstone of the data modeling domain.
Writing Common DAX Functions and Measures
To succeed on the PL-300 exam, you need to be comfortable writing DAX formulas using a variety of common functions. The exam will not require you to know every function in the library, but it will expect you to be proficient with the most important ones. This starts with basic aggregator functions like SUM, AVERAGE, COUNT, and DISTINCTCOUNT. You should be able to write simple measures that use these functions to summarize columns from your fact tables, such as creating a [Total Sales] measure using the SUM function on the SalesAmount column.
Beyond simple aggregations, you must master what is arguably the most important function in DAX: CALCULATE. This function allows you to modify the filter context in which a calculation is performed. It is the key to creating more complex and powerful measures. You will be expected to use CALCULATE to apply filters to your measures, for example, to calculate the sales for a specific region or product category. Understanding how to use CALCULATE in conjunction with other functions like FILTER and ALL is critical for advanced analysis.
The exam will also cover time intelligence functions. These functions make it easy to perform common date-based calculations, such as year-to-date (YTD), quarter-to-date (QTD), and comparisons with previous periods. Functions like DATESYTD, SAMEPERIODLASTYEAR, and DATEADD are essential tools for any data analyst. To use these functions effectively, you must have a properly configured date dimension table in your model, which is another key concept the exam will assess. Proficiency with these common DAX patterns demonstrates your ability to add significant analytical depth to your data models.
Optimizing Model Performance
Building a data model that is accurate is only half the battle; it must also be performant. A slow, unresponsive report leads to a poor user experience and low adoption rates. The PL-300 exam includes topics related to model performance optimization, testing your ability to create models that are not only powerful but also efficient. One of the primary ways to optimize performance is by reducing the size of your data model. A smaller model consumes less memory and results in faster query processing.
There are several techniques to reduce model size that you will be expected to know. This includes removing unnecessary columns and rows during the data preparation phase in Power Query. Every column you import into your model has a cost in terms of memory, so it is crucial to only bring in the data that is absolutely necessary for your analysis. You should also pay attention to the data types of your columns, as choosing the most appropriate data type can also save space. For example, using a whole number data type instead of a decimal where possible is more efficient.
Another key optimization technique relates to the cardinality of your columns. Columns with a high number of unique values, like primary key columns or high-precision date-time columns, are more expensive to store. You should look for opportunities to reduce cardinality, for example, by splitting date-time columns into separate date and time columns, or by disabling the auto date/time feature in Power BI. Understanding and applying these optimization strategies will demonstrate your ability to build enterprise-grade data models that perform well even with large volumes of data.
Enhancing the Data Model with User-Friendly Features
A technically perfect data model is of little use if business users find it confusing or difficult to navigate. A portion of the data modeling domain focuses on enhancing the model to improve its usability. The PL-300 exam will assess your ability to implement features that create a more intuitive experience for report creators and consumers. This includes organizing your model by creating display folders to group related measures and columns, making it easier for users to find what they are looking for in the Fields pane.
Another important aspect is formatting your data correctly. You should know how to apply formatting to your measures and columns, such as specifying currency symbols, percentage signs, and the number of decimal places. This ensures that data is presented consistently and professionally across all reports that are built on top of the model. You should also be able to set default summarization properties
