The DP-600 exam is aimed at those who want to build and demonstrate expertise in the overall breadth of Microsoft Fabric, with a technical focus on SQL, KQL (Real Time Intelligence) and DAX. Click here for the current syllabus, review the three-part series of articles here on the official Microsoft Fabric community site and see the 2.5 hour DP-600 Prep session with other community leaders here!
Practice questions are one of the best ways to actively learn the material and identify/fill knowledge gaps - click on any of the modules below to take a 5 question practice quiz.
Once you take a quiz attempt below you will be emailed with the results!
Note that in order to discourage copying of questions into unauthorised "exam dumps" the number of quiz attempts will be restricted to 5 per month for each module (15 per month in total)
Practice exam questions in this area will test your ability to connect to and explore various data sources. You might face scenarios requiring you to choose the best storage format—lakehouse, warehouse, or eventhouse—based on specific data needs. Questions will assess your understanding of data ingestion, accessing real-time data, and integrating OneLake to manage and optimize data workflows effectively.
Questions will focus on applying data transformation techniques. Expect tasks that involve creating views, functions, and stored procedures, as well as enriching datasets with additional columns or tables. You'll encounter scenarios that test your ability to implement star schemas, resolve data quality issues, and perform operations like aggregation, joins, and data type conversions to prepare data for analysis.
Practice questions will challenge you to query data using tools like Visual Query Editor, SQL, and KQL, focusing on selecting, filtering, and aggregating data accurately. You’ll be tested on your skills in building semantic models, implementing relationships, and writing DAX calculations. Additional scenarios might involve optimizing data performance, configuring refresh settings, and handling large-scale data models for reporting.
