Microsoft DP-600 prep: Fabric Analytics Engineer Associate

learn to download and install power BI desktop, choose the 64-bit version, and activate a free or pro license for the power BI service with fabric.

Enable the on object interaction preview in Power BI Desktop, switch between data, formatting, and analytical panes, insert visuals, and adapt pane layouts across versions.

Create visuals in Power BI Desktop from an Excel workbook and load data. Explore a stacked column chart with a date hierarchy and drill from year to quarter to month.

Publish your visualization and semantic model to the Power BI service from Power BI desktop, using a Microsoft Fabric Free account to access your workspace.

Learn why Power BI and Fabric require a work or school email, and explore practical paths to obtain one, free Microsoft 365 Business Basic, and option to buy a domain.

Learn to load data from a Fabric data workbook and build a stacked column chart in Power BI. Save the report locally and publish it to the Power BI service.

Create calculated columns using basic operators to transform data, rename columns, and format currency in the table view, including converting amount from pounds to dollars and using date hierarchies.

Learn how the if, blank, and isblank functions work in DAX to control values, handle blank results, and create calculated columns in Power BI.

Explore how the and, or, and not functions in DAX work with two-condition examples, including between 60 and 69, using brackets, ampersands, and the vertical bar.

Learn to replace nested ifs with the switch function in Power BI by mapping organization key values to regions like northeast, northwest, central, southeast, and southwest, with an optional else.

Explore the logical functions false, true, and iferror, and the divide function in DAX to handle division by zero and return blanks or alternate values.

Practice activity 2 guides you to evolve your model with calculated columns such as location name, is us, and continent mapping, using logical functions and basic operators in fabric analytics.

Explore aggregation and iterator functions in Power BI, including sum, average, count, max, min, median, count blank, and learn implicit versus explicit measures.

Learn how iterator functions such as average x, count x, max x, median x, and min x compute a table's expression with two arguments—the table and the expression—avoiding helper columns.

Explore DAX statistical functions in Power BI for in-memory table manipulation, including add columns, count rows, distinct count, percentiles, and ranks with rank.eq and rank.x.

Demonstrates creating a calculated column and a measure to average processing fees by country with a switch, and ranks sales territories using rank.eq in a dim geography model.

Learn to expand a semantic model by importing multiple tables from an Excel workbook, creating and managing relationships, and building a star schema for accurate aggregations.

Explore how only one active relationship governs many-to-one tables; switch inactive relationships and cross-filter direction from single to both to ensure correct measures and visuals.

Learn how information functions in DAX work, focusing on is error and if error to handle cascading errors, and on lookup value for single-value lookups with optional alternate results.

Add a dim geography and dim sales territory table, create a relationship on sales territory key, and build calculated columns using lookup value and iferror to derive country by group.

Master the related function in Power BI, which fetches a column via existing relationships. Learn when to prefer related over the lookup value function and how hiding tables reduces complexity.

Use the related table function to aggregate from one to many in Power BI. Compute sum of amount in fact finance and apply count x, count rows, with context.

Explore how context governs calculations with related tables, sumx versus sum, and the distinction between calculated columns and measures, including context-driven visuals and percentages.

Learn how the all function removes filters to compute a percent of total from the fact finance table, ignoring context by using a measure instead of a calculated column.

Explore how the filter function narrows a table expression to a specific context, such as year 2028, by creating a measure like amount 2028 from the fact finance table.

Discover how the calculate function reshapes context in Power BI by applying all or filter to an expression, keeping your original formula intact.

Explore the allexcept function within a calculate expression to retain specific filters like organization name and date year, and compare actual and budget values.

Explore how the Allselected function in Power BI retains explicit filters while removing context filters from specified columns or tables, producing accurate visual totals.

Explore practical DAX filter functions beyond the basics, including cross filter for single or bidirectional direction, distinct for unique values, and keep filters to combine nested calculate contexts.

Import the Fabric Data workbook, load dim customer, create a many-to-one geography relationship with cross-filter direction both, then build calculated columns and measures to analyze regional demographics in a matrix.

Explore date time and time intelligence functions and learn to create a date table in Power BI using calendar or calendar auto, with proper dating and relationships.

Learn to use the USERELATIONSHIP function to activate an inactive relationship, apply the EDATE function to shift dates by months, and use CROSSFILTER to adjust bidirectional filtering.

Explore time intelligence functions to build running totals with year-to-date, month-to-date, and quarter-to-date calculations using dax, calculate, date add, same period last year, and parallel period.

Build a date-first-purchase visualization from the dim customer table, add measures such as year start, running total, prev year, and current month, and use a date table for relationships.

Identify bottlenecks with the Performance Analyzer and optimize queries, visuals, and DAX performance. Apply strategies to reduce granularity, limit visuals, and simplify models, including cross-filter direction and snowflake-to-star schema.

Explore how to choose a storage mode in Power BI, comparing import, direct query, and dual, and understand how query folding affects performance with an Azure SQL database.

Learn how query folding improves Power BI query performance by pushing transformations to SQL Server via native queries, reducing data load and leveraging server-side operations.

Explore the Power BI service interface, publish and view semantic models and reports in your workspace, and learn to create, edit, and save dashboards and reports.

Create a new workspace and configure access controls with admin, member, contributor, and viewer roles; assign Power BI Pro, premium per user, fabric capacity licenses, and manage apps, semantic models, dataflows, gateways.

implement incremental refresh for large semantic models in Power BI by using range start and range end parameters and refreshing in the service with date time filters.

Implement incremental refresh for the semantic model by using Rangestart and Rangeend to filter the product table. Publish to the Power BI service and configure credentials to enable incremental refresh.

Learn item-level access controls by sharing a report, setting view and build permissions, and managing direct access and reshare rights across the workspace and the semantic model.

Explore how to design clean data models with first normal form, enforcing atomic values and single-valued fields, by separating subcategories and categories in snowflake versus star schemas for Power BI.

Move repeating data into dedicated tables to achieve second normal form. Relate orders and products with foreign keys, and separate subcategories and categories for third normal form in Power BI.

Implement a star schema for a Power BI semantic model, denormalizing from snowflake, using Power Query merge queries and left outer joins to optimize large datasets.

Demonstrates implementing a star schema in Power BI by building relationships, adding calculated columns using related, and visualizing sales by territory and year in a matrix.

Discover how calculation groups simplify time intelligence in Power BI by creating a single year-to-date measure for sales and orders, and enable the preview feature in Power BI Desktop.

Create a calculation group with a time intelligence item and a dim date table. Connect it to the fact table, mark as date, and create a year-to-date measure.

Add and reorder calculation items to show total, year to date, quarter to date, month to date, and a year over year calculation using time intelligence in a matrix.

Use a calculation group with a slicer to let users select measures, apply dynamic format strings for year-over-year percentages, and implement explicit measures in tables or matrices.

Enable field parameters in Power BI to let users switch explicit measures in visuals via a slicer, using measures like sales amount, order quantity, and extended amount.

Explore building a date table and dim date relationships, create explicit measures and a time intelligence calculation group, and compare this year to the previous year with yearly percentage change.