DP-500 key terms

A data model design with a central fact table containing measurable events and related dimension tables that provide descriptive context. It simplifies queries and improves Power BI performance by reducing joins and enabling efficient filtering.

A normalized extension of a star schema where dimension tables are split into multiple related tables. It reduces data redundancy but increases query complexity and may degrade Power BI performance due to additional joins.

A table in a dimensional model that stores quantitative measurements (facts) for business events, such as sales amounts or quantities. It typically contains foreign keys to dimension tables and is the core of a star schema.

A table that provides descriptive attributes (dimensions) used to filter, group, and label facts. Examples include Customer, Product, and Date. Dimension tables contain a primary key that links to foreign keys in fact tables.

A system-generated integer key assigned to each row in a dimension table, used instead of natural or business keys. It ensures uniqueness and stability, and improves join performance in data models.

A key derived from business data, such as a customer number or product code, that uniquely identifies a record. It may change over time, so surrogate keys are often used in dimensional modeling.

A technique for managing changes in dimension attributes over time. Type 1 overwrites old values, Type 2 adds new rows with versioning, and Type 3 adds new columns. Type 2 is common for historical reporting.

A modeling approach for enterprise data warehouses that uses hubs (business keys), links (relationships), and satellites (descriptive attributes) to provide auditability, scalability, and flexibility for integrating disparate sources.

In Power BI, a DAX calculation evaluated at query time based on filter context. Measures are dynamic and not stored in the model, making them ideal for aggregations like sums, averages, and ratios.

A column in a Power BI table whose values are computed using DAX and stored during data refresh. It increases model size and is evaluated row by row, unlike measures which are computed at query time.

The formula language used in Power BI, Analysis Services, and Power Pivot for creating calculations, measures, and calculated columns. It includes functions for aggregation, filtering, time intelligence, and relationship navigation.

The set of filters applied to a calculation at evaluation time, determined by slicers, visual axes, and cross-filtering. It controls which rows are considered by a measure and is central to DAX behavior.

The context in DAX where a calculation is evaluated row by row, such as in calculated columns or iterators like SUMX. It does not automatically propagate through relationships unless combined with filter context.

A DAX function that evaluates an expression in a modified filter context. It is used to override or add filters, enabling complex calculations like year-to-date totals or conditional aggregations.

A set of DAX functions that simplify calculations over time periods, such as year-to-date, month-over-month growth, and moving averages. These functions rely on a marked date table for correct results.

A dedicated table in the Power BI model containing a continuous range of dates with attributes like year, quarter, and month. It must be marked as a date table to enable accurate time intelligence calculations.

A Power BI security feature that restricts data access for users based on filters defined in roles. It can be static (fixed values) or dynamic (using USERNAME() or CUSTOMDATA()) and is enforced in the Power BI service.

A row-level security implementation where the filter logic uses functions like USERNAME() or USERPRINCIPALNAME() to dynamically restrict data based on the authenticated user, often combined with a security bridge table.

A security feature in Power BI that restricts access to specific tables or columns for certain users or roles. It is configured in Tabular Editor or XMLA endpoints and hides sensitive data from unauthorized users.

A Microsoft Purview Information Protection label applied to Power BI items (reports, datasets, dashboards) to classify and protect data. Labels can enforce encryption and persist when data is exported to Excel or other formats.

A set of policies in Microsoft Purview that detect and restrict sharing of sensitive information in Power BI, such as exporting data to external locations or sharing with unauthorized users based on sensitivity labels.

A container in the Power BI service for collaborating on dashboards, reports, datasets, and dataflows. Workspaces can be assigned to capacities and have roles (Admin, Member, Contributor, Viewer) to manage access.

A packaged, read-only distribution of content from a Power BI workspace to a broad audience. Apps simplify sharing by providing a single entry point and can be updated with new content versions.

A Power BI feature that enables staged deployment of content across development, test, and production workspaces. It supports version control and controlled promotion of reports and datasets.

A dedicated Power BI capacity (P SKU or Microsoft Fabric F SKU) that provides reserved resources, higher limits, and advanced features like larger dataset sizes, deployment pipelines, and paginated reports.

A Power BI model that combines data from different sources, including DirectQuery and imported tables, with relationships. It enables large-scale models and real-time access while maintaining performance.

A Power BI connection mode where queries are sent to the source database at query time, avoiding data import. It is suitable for large or frequently changing data but may have slower performance and limited DAX functions.

A pre-aggregated table in a Power BI model that stores summarized data to improve query performance for large datasets. It is often used with DirectQuery in a composite model to balance speed and detail.

A cloud-based ETL feature in Power BI that extracts, transforms, and loads data into Azure Data Lake Storage. Dataflows can be reused across datasets and support Power Query Online transformations.

The data transformation and preparation engine in Power BI, Excel, and Azure Data Factory. It uses M language to connect to sources, clean, shape, and load data into models or dataflows.

The formula language used in Power Query for data transformation. It is case-sensitive and functional, enabling complex data shaping, custom functions, and parameterization.

A cloud-based analytics service that integrates big data and data warehousing. It supports SQL pools, Spark pools, and pipelines, and is often used as a source for Power BI enterprise models.

A centralized repository that stores raw data in its native format, often on Azure Data Lake Storage Gen2. It supports scalable analytics and is used by services like Azure Synapse and Power BI dataflows.

A structured repository optimized for reporting and analysis, typically modeled with star or snowflake schemas. It consolidates data from multiple sources and serves as a source for Power BI datasets.

A read/write endpoint in Power BI Premium that allows external tools like Tabular Editor, SQL Server Management Studio, and ALM Toolkit to connect to and modify datasets, enabling advanced modeling and deployment.

A third-party tool for editing Power BI tabular models via XMLA endpoints. It supports advanced scripting, calculation groups, object-level security, and best practice rules for model development.

A Power BI modeling feature that defines reusable calculation items (e.g., time intelligence, currency conversion) applied to measures. It simplifies complex models and improves maintainability.

The ability of Power Query to translate transformations into native queries executed by the data source. It improves performance by reducing data transfer and leveraging source compute.

The in-memory columnar database engine used by Power BI and Analysis Services Tabular models. It provides high compression and fast query performance through columnar storage and advanced indexing.