Topic 1 — Power BI Basics & Architecture

Power BI is a business intelligence platform from Microsoft that enables users to connect to data, transform it, create interactive reports, and share insights through dashboards.
Power BI consists of Desktop (report building), Service (sharing & collaboration), Mobile apps, Gateway (on-prem connectivity), and Report Builder (paginated reports).
Power BI Desktop is used to connect to data, transform it, build data models, and create reports. Power BI Service is the cloud platform used to publish reports, share dashboards, schedule refresh, and collaborate with other users.
A report is a collection of one or more pages containing visuals such as charts, tables, and maps. Reports are interactive and allow filtering, drill-down, and cross-highlighting of data.
A dashboard is a single-page canvas created in Power BI Service. It contains pinned visuals (tiles) from one or multiple reports and is mainly used for high-level monitoring of KPIs.
A dataset is the underlying data model used to create reports. It includes tables, relationships, calculated columns, and measures. Reports and dashboards are built on top of datasets.
In Import mode, data is loaded into Power BI memory, offering very fast performance. In DirectQuery mode, data stays in the source system and Power BI sends live queries, which is useful for real-time or very large datasets.
Power BI Gateway is used to securely connect on-premises data sources to Power BI Service. It allows scheduled refresh and live queries without moving data to the cloud.
Workspaces are collaborative areas in Power BI Service where teams can create, store, and manage datasets, reports, dashboards, and apps together.
Power BI provides Visual-level filters, Page-level filters, Report-level filters, and Drill-through filters to control data visibility and navigation.
A semantic model represents business logic on top of raw data. It includes relationships, measures, hierarchies, and calculations, enabling consistent analysis across reports.
This can happen due to large datasets, complex DAX calculations, high-cardinality columns, gateway latency, or limited Power BI Service capacity.
Common reasons include failed scheduled refresh, gateway issues, expired credentials, or the dataset not being refreshed after updates.
Reports can be shared using workspace roles, app publishing, direct sharing (with Pro license), and Row-Level Security (RLS) to restrict data access.
Real-time dashboards can be built using DirectQuery, streaming datasets, push datasets, or Power BI REST APIs.
A tile is an individual visual element pinned to a dashboard, such as a chart, card, KPI, image, or text box.
Power BI offers Free, Pro, Premium Per User (PPU), and Premium capacity licenses depending on sharing and performance needs.
Q&A allows users to ask questions in natural language and Power BI automatically generates visuals based on the query.
VertiPaq is Power BI’s in-memory columnar storage engine that compresses data and enables very fast analytical queries.
The typical workflow includes connecting to data, transforming data in Power Query, building the data model, creating visuals, publishing to the service, scheduling refresh, and sharing dashboards.

Topic 2 — Data Sources & Data Import

Power BI supports a wide range of data sources including Excel, CSV, SQL Server, MySQL, PostgreSQL, Oracle, SharePoint, Azure SQL, Azure Blob Storage, Web APIs, Google Analytics, and many other cloud and on-premises sources.
File-based sources store data in files such as Excel, CSV, XML, or JSON. Database sources store data in structured systems like SQL Server, Oracle, or MySQL and allow querying large volumes of data efficiently.
In Import mode, data is loaded into Power BI’s in-memory engine (VertiPaq). This provides high performance and rich analytical capabilities but requires refresh to update data.
DirectQuery mode keeps data in the source system and sends queries in real time. It is useful for very large datasets or when near real-time data is required, but has performance and modeling limitations.
Live Connection is used to connect directly to Analysis Services or Power BI datasets. No data is imported, and modeling is done at the source.
Import loads data into Power BI memory for best performance. DirectQuery queries data live from the source. Live Connection connects directly to an existing semantic model without allowing local data modeling.
Data refresh updates the imported dataset with the latest data from the source. It can be done manually or scheduled automatically in Power BI Service.
Power BI supports manual refresh, scheduled refresh, on-demand refresh, and incremental refresh for large datasets.
Incremental refresh refreshes only new or changed data instead of the entire dataset. It improves performance and reduces refresh time for large historical datasets.
Credentials store authentication details such as username, password, OAuth, or API keys required to access a data source securely.
If credentials expire, data refresh fails. The dataset must be re-authenticated in Power BI Service to resume scheduled refresh.
Yes, Power BI can combine data from multiple sources using Power Query. This process is known as data blending and is commonly used in real-world projects.
A Web data source allows Power BI to retrieve data from web pages, REST APIs, or JSON/XML endpoints over HTTP or HTTPS.
A data gateway is required when Power BI Service needs to access on-premises data sources for scheduled refresh or live queries.
Personal Gateway is for individual use and single user scenarios. Enterprise Gateway supports multiple users and data sources and is suitable for organizational deployments.
Query folding pushes data transformation steps back to the source system. This improves performance by reducing the amount of data processed in Power BI.
Query folding reduces memory usage, improves refresh speed, and ensures transformations are handled efficiently by the source database.
Privacy levels (Public, Organizational, Private) control how data from different sources can be combined to prevent data leakage.
Dataflows are cloud-based ETL processes that use Power Query Online. They allow reusable data transformations across multiple datasets.
Dataflows should be used when multiple reports need the same cleaned data, or when centralizing data preparation for governance and scalability.

Topic 3 — Power Query (ETL & M Language)

Power Query is the data transformation and ETL (Extract, Transform, Load) engine in Power BI. It is used to clean, shape, merge, and prepare raw data before loading it into the data model.
ETL stands for Extract, Transform, and Load. Power BI extracts data from sources, transforms it using Power Query, and loads the cleaned data into the Power BI data model.
M is the functional programming language used by Power Query. Every transformation applied in the Power Query Editor is written as an M expression behind the scenes.
Power Query Editor is the interface where users perform data cleaning and transformation tasks such as filtering rows, changing data types, splitting columns, and merging tables.
Applied Steps represent the sequence of transformations applied to data. Each step corresponds to an M expression and is executed in order during refresh.
Query folding is the process where Power Query pushes transformation logic back to the data source so that the source system performs the computation.
Query folding improves performance, reduces memory usage, and speeds up refresh by minimizing data transferred to Power BI.
In Power Query Editor, right-click a step and check if “View Native Query” is enabled. If available, query folding is occurring.
Merge Queries joins tables horizontally based on a key. Append Queries combines tables vertically by stacking rows.
Parameters allow dynamic values such as dates, file paths, or thresholds to be passed into queries, making solutions flexible and reusable.
Close & Apply saves transformations and loads data into the model. Disabling load keeps the query for reference without loading it into the dataset.
Data profiling provides column quality, column distribution, and column profile insights to understand data completeness and patterns.
Duplicates can be removed by selecting one or more columns and choosing “Remove Duplicates” from the ribbon.
Missing values can be removed, replaced, or filtered using Power Query options such as Replace Values or Fill Down.
A custom column allows users to create new columns using formulas written in M language to derive values from existing columns.
Power Query is used for data preparation before loading data. DAX is used for calculations and measures after data is loaded into the model.
Data types define how values are stored and processed. Correct data types improve performance and prevent calculation errors.
Functions are reusable pieces of M code that accept inputs and return outputs. They are useful for applying the same logic across multiple queries.
During refresh, Power Query re-executes all applied steps in sequence to fetch and transform the latest data from the source.
Transformations such as cleaning, filtering, joining, and reshaping should be done in Power Query to improve performance and simplify the data model.

Topic 4 — Data Modeling

Data modeling in Power BI is the process of structuring data using tables, relationships, keys, and calculations so that it can be analyzed efficiently and accurately.
Good data modeling improves performance, simplifies DAX calculations, ensures accurate results, and makes reports easier to maintain and scale.
A star schema consists of one central fact table connected to multiple dimension tables. It is the recommended modeling approach in Power BI for better performance and simplicity.
A fact table contains quantitative data such as sales, revenue, quantity, or transactions. It usually includes foreign keys linking to dimension tables.
A dimension table contains descriptive attributes such as customer name, product category, date, or region, which provide context to fact data.
Relationships define how tables are connected using key columns. They allow Power BI to filter and aggregate data across tables correctly.
Power BI supports one-to-many, many-to-one, many-to-many, and one-to-one relationships.
Cardinality describes how rows in one table relate to rows in another, such as one-to-many or many-to-many.
Cross-filter direction determines how filters flow between related tables. It can be single-direction or both-direction (bi-directional).
Bi-directional relationships should be used carefully, typically in many-to-many scenarios or complex filtering needs, as they can impact performance and cause ambiguity.
An active relationship is used by default in calculations. An inactive relationship exists but must be activated using DAX functions like USERELATIONSHIP.
USERELATIONSHIP is a DAX function that activates an inactive relationship for a specific calculation.
A surrogate key is an artificial unique identifier (often numeric) used instead of natural keys to improve performance and consistency.
Normalization reduces data redundancy by splitting tables. Denormalization combines data for faster querying, which is often preferred in Power BI models.
A role-playing dimension is a single dimension table used multiple times in different roles, such as Order Date, Ship Date, and Delivery Date.
A date table is a dedicated calendar table required for time intelligence calculations like YTD, MTD, and YoY analysis.
Without a proper date table, time intelligence functions may not work correctly and results can be inaccurate.
Model ambiguity occurs when multiple filter paths exist between tables, causing Power BI to be unable to determine which path to use.
Model optimization involves reducing columns, removing unused tables, using correct data types, applying star schema, and avoiding unnecessary bi-directional relationships.
Best practices include using star schema, a proper date table, simple relationships, minimal columns, and performing transformations in Power Query rather than DAX where possible.

Topic 5 — DAX (Basic to Advanced)

DAX (Data Analysis Expressions) is a formula language used in Power BI to create calculated columns, measures, and tables for data analysis.
Calculated columns are computed row by row and stored in the model. Measures are calculated at query time based on filters and are not stored.
Row context refers to the current row being evaluated. It is automatically created in calculated columns and iterators.
Filter context is the set of filters applied to a calculation from visuals, slicers, page filters, or DAX expressions.
Context transition occurs when CALCULATE converts row context into filter context during measure evaluation.
CALCULATE modifies filter context to evaluate expressions under specific conditions and is the most powerful DAX function.
ALL removes all filters from a table or column. ALLEXCEPT removes all filters except the specified columns.
SUM aggregates a column directly. SUMX iterates row by row, evaluates an expression, and then sums the results.
Iterator functions (SUMX, AVERAGEX, FILTER) evaluate expressions row by row over a table.
FILTER returns a table based on conditions and is often used inside CALCULATE for complex filtering.
COUNT counts numeric values, COUNTA counts non-blank values, DISTINCTCOUNT counts unique values.
Time intelligence functions perform calculations over time, such as YTD, MTD, YoY, and require a proper date table.
TOTALYTD calculates year-to-date values based on a date column and filter context.
RELATED retrieves values using an existing relationship. LOOKUPVALUE searches based on matching column values without requiring a relationship.
EARLIER is used in nested row contexts to access a value from an outer row context.
A calculated table is created using DAX and is evaluated during data refresh.
It involves reducing iterators, using measures instead of columns, avoiding unnecessary FILTERs, and optimizing data models.
HASONEVALUE checks if a single value exists in context. VALUES returns a table of unique values.
ISBLANK checks whether a value is blank. BLANK() returns a blank value.
Use measures over columns, keep formulas simple, follow star schema, use proper date tables, and test performance using Performance Analyzer.

Topic 6 — Data Visualization

Data visualization is the graphical representation of data using charts, tables, maps, and KPIs to help users understand trends, patterns, and insights easily.
Common visuals include bar charts, column charts, line charts, pie charts, tables, matrices, cards, KPIs, maps, and slicers.
A table displays flat data in rows and columns. A matrix supports hierarchies, drill-down, and grouping of rows and columns.
A slicer is a visual filtering tool that allows users to interactively filter data across visuals on a report page.
A KPI visual shows a key metric along with its target and trend to quickly assess performance.
Conditional formatting dynamically changes colors, icons, or data bars based on rules or values to highlight insights.
Custom visuals are visuals created by Microsoft or third parties and imported from AppSource to extend visualization capabilities.
Drill-down allows navigation within a visual hierarchy. Drill-through navigates to a detailed page using selected context.
Cross-filtering filters other visuals completely. Cross-highlighting highlights related data while dimming the rest.
Tooltips display additional information when hovering over visuals and can include custom tooltip pages.
A report theme is a JSON file that defines colors, fonts, and formatting to maintain consistent design across reports.
Bookmarks capture the state of visuals, filters, and navigation to create interactive storytelling experiences.
The selection pane controls visibility, layering, and interaction of visuals on a report page.
Use appropriate chart types, avoid clutter, maintain consistency, highlight key insights, and follow a clear visual hierarchy.
Choose visuals based on data type and purpose: comparisons, trends, distributions, proportions, or relationships.
Responsive design automatically adjusts visual layout based on screen size, especially for mobile devices.
Small multiples display the same visual repeated across categories, making comparisons easier.
Data storytelling combines visuals, insights, and narrative to guide users toward meaningful conclusions.
A report is multi-page and interactive. A dashboard is a single-page summary built in the Power BI Service.
Avoid using too many visuals, incorrect chart types, excessive colors, cluttered layouts, and misleading scales.

Topic 7 — Power BI Service

Power BI Service is the cloud-based platform where reports are published, shared, refreshed, and consumed by users through a web browser or mobile app.
In Power BI Service, you can share reports, create dashboards, configure scheduled refresh, manage workspaces, publish apps, and apply security settings.
A workspace is a collaborative environment used to store datasets, reports, dashboards, dataflows, and apps for a team or project.
An App is a packaged collection of reports and dashboards published from a workspace and shared with end users.
A report is multi-page and interactive. A dashboard is a single-page summary made by pinning visuals from reports in Power BI Service.
Scheduled refresh automatically updates imported datasets at defined intervals to ensure reports show the latest data.
An on-premises data gateway enables secure communication between Power BI Service and on-premises data sources.
Personal gateway is for individual use. Standard gateway supports multiple users and datasets and is recommended for enterprise environments.
Refresh failure occurs when Power BI cannot retrieve data due to credential issues, gateway problems, source unavailability, or query errors.
Row-Level Security restricts data visibility so users can only see rows they are authorized to view.
RLS is defined in Power BI Desktop using roles and filters, then published and assigned to users in Power BI Service.
Content can be shared using workspace access, direct sharing links, or by publishing apps.
Power BI Premium provides dedicated capacity, enhanced performance, larger datasets, and advanced enterprise features.
PPU is a per-user license that provides many Premium features without requiring dedicated capacity.
Deployment pipelines help manage report lifecycle across development, test, and production environments.
Dataflows are cloud-based Power Query transformations that can be reused across multiple datasets.
Lineage view shows how datasets, reports, dashboards, and dataflows are connected.
Usage metrics show how often reports and dashboards are viewed, helping measure adoption and performance.
The Power BI mobile app allows users to view dashboards and reports on mobile devices.
Use apps for distribution, limit workspace access, monitor refresh failures, apply security, and manage environments using deployment pipelines.

Topic 8 — Security & Governance

Security in Power BI ensures that users can access only the data, reports, and dashboards they are authorized to see, protecting sensitive business information.
Governance refers to policies, processes, and controls that manage how Power BI content is created, shared, secured, and maintained across an organization.
Row-Level Security restricts data access at the row level, ensuring users see only the records they are permitted to view.
Object-Level Security hides specific tables, columns, or measures from users, providing more granular control than RLS.
Workspace roles (Admin, Member, Contributor, Viewer) control what actions users can perform within a workspace.
Viewers can only view content. Members can create, edit, and publish content within the workspace.
Dataset permissions control who can build reports, view data, or reshare datasets in Power BI Service.
Sensitivity labels classify data (Confidential, Public, Restricted) and help enforce data protection policies.
Tenant-level settings define global Power BI behavior, such as export permissions, sharing rules, and external user access.
DLP policies prevent sensitive data from being shared or exported outside the organization.
Auditing tracks user activities such as viewing, sharing, exporting, and modifying content for compliance and monitoring.
The audit log records all Power BI activities and can be accessed through Microsoft 365 compliance tools.
Endorsement marks datasets as Promoted or Certified to indicate trusted and verified data sources.
Least privilege means users are granted only the minimum access necessary to perform their job.
Best practices include clear ownership, certified datasets, restricted sharing, auditing usage, and enforcing security policies.
The Admin Portal allows administrators to manage tenant settings, capacity, users, and governance controls.
Security can be enforced using Azure AD B2B, restricted sharing, RLS, and tenant-level settings.
Data ownership defines responsibility for maintaining dataset accuracy, refresh, and access control.
Compliance ensures Power BI usage meets regulatory and organizational standards such as GDPR or internal policies.
Common mistakes include over-sharing, not using RLS, ignoring tenant settings, lack of auditing, and unmanaged workspaces.

Topic 9 — Performance Optimization

Performance optimization means improving report responsiveness, reducing load time, and ensuring fast visual interactions by optimizing data model, DAX, and visuals.
Common causes include poor data modeling, high-cardinality columns, complex DAX, excessive visuals, and inefficient DirectQuery sources.
A star schema reduces relationship complexity, simplifies filter propagation, and allows the VertiPaq engine to work more efficiently.
VertiPaq uses columnar storage and compression techniques to store data efficiently in memory, enabling very fast analytical queries.
High-cardinality columns increase memory usage, reduce compression efficiency, and slow down queries.
Performance Analyzer measures visual load time, DAX query duration, and rendering time to help identify bottlenecks.
Import mode is faster because data is stored in memory. DirectQuery performance depends on the source system and network latency.
Performing data cleaning, filtering, and aggregation in Power Query reduces the size and complexity of the model.
Query folding pushes transformations to the data source, reducing data transfer and improving refresh speed.
Inefficient DAX with unnecessary iterators, complex filters, or calculated columns can significantly slow down reports.
Measures are calculated at query time and do not increase model size, while calculated columns consume memory.
Each visual sends a query to the model, so too many visuals increase rendering time and slow page performance.
Slicers with high-cardinality fields or many slicers on a page can slow down interactions.
Incremental refresh updates only new or changed data instead of reloading the entire dataset, improving refresh performance.
Aggregation tables store summarized data to speed up queries on large datasets.
Dual mode allows tables to act as both Import and DirectQuery, improving query performance in composite models.
Remove unused columns and rows, use correct data types, reduce cardinality, and pre-aggregate data.
Proper indexing in source databases significantly improves DirectQuery performance by speeding up SQL execution.
Bookmarks can reduce visual rendering by hiding unnecessary visuals and controlling report navigation.
Use star schema, optimize DAX, limit visuals per page, use Import mode when possible, and monitor performance using Performance Analyzer.

Topic 10 — Real-World Scenarios

For senior management, I would focus on high-level KPIs, trends, and exceptions. The report would be minimal, visually clean, use summary cards, trend charts, and allow drill-through for deeper analysis when required.
I handle large datasets using Import mode with optimization, incremental refresh, aggregation tables, reducing columns, and using a star schema to improve performance.
I would analyze the report using Performance Analyzer, check model design, simplify DAX measures, reduce visuals per page, and verify storage mode.
I design flexible models, use measures instead of calculated columns, parameterize Power Query steps, and communicate with stakeholders to manage expectations and changes effectively.
I use Row-Level Security for data access, workspace roles for content access, and sensitivity labels for data classification.
I choose Import mode for performance and analytics. DirectQuery is chosen only when real-time data or very large datasets are required.
I validate data with source systems, use data profiling in Power Query, reconcile totals, and implement certified datasets.
I integrate multiple sources using Power Query, ensure consistent keys, build a unified data model, and handle relationships carefully to avoid ambiguity.
I focus on business language, visual storytelling, clear labels, tooltips, and simple explanations rather than technical details.
I use tables, matrices, drill-through, export options, and explain Power BI’s analytical advantages compared to Excel.
I use deployment pipelines, separate workspaces for Dev/Test/Prod, and controlled publishing to ensure stability.
I check gateway status, credentials, error logs, and source availability, then notify stakeholders if delays occur.
I use Power BI usage metrics, audit logs, and feedback from users to understand adoption and improve reports.
I balance performance and usability by limiting visuals, optimizing DAX, using consistent layouts, and prioritizing key insights.
I assess impact, prioritize critical changes, test thoroughly, communicate risks, and avoid introducing instability.
I conduct requirement discussions, share prototypes, gather feedback early, and iterate based on business needs.
Common challenges include unclear requirements, data quality issues, performance tuning, and managing user expectations.
I document data sources, model design, key measures, refresh schedules, and security rules for maintainability.
I design scalable models, reusable measures, certified datasets, and follow best practices to accommodate future growth.
Power BI integrates data, analytics, security, and visualization in one platform, making it ideal for real-world decision-making.