Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)

You should have hands-on experience working with data, ideally in an analytics or engineering role. Familiarity with SQL is expected, and some exposure to Power BI or Azure data services will help. If you have never written a DAX formula or built a data model before, plan to spend extra time on the semantic model sections.

The DP-600 underwent a major overhaul to clearly separate Analytics Engineering from pure Data Engineering (which is now covered by the newer DP-700 exam). If you are looking at older study materials, be aware that PySpark and deep notebook data transformations have been completely removed from the DP-600. The updated exam replaces those with a heavier emphasis on Real-Time Intelligence (like Eventhouse and KQL), security, and semantic model optimization. It is now a much more focused certification that accurately reflects the day-to-day responsibilities of an analytics engineer, rather than testing heavy data engineering tasks.

The exam has around 40 to 60 questions, including multiple choice, case studies, and scenario-based questions. Microsoft weights data preparation most heavily at 45 to 50 percent, so expect a significant portion of questions on ingestion, transformation, and querying. Security and semantic model topics split the remaining weight roughly evenly. The exam is proctored and runs about 100 minutes. However, Microsoft schedules a 120-minute "seat time" block to account for the NDA, instructions, and surveys.

For most people in analytics engineering roles, yes. The DP-600 maps closely to real job responsibilities rather than testing abstract theory, so preparing for it often surfaces gaps in areas like XMLA endpoint management or Direct Lake configuration that practitioners overlook in day-to-day work. It also signals to employers that your Fabric skills have been validated against a consistent standard.

Yes. The course addresses KQL for querying data and covers Eventhouse integration with OneLake, which is part of the Prepare Data skill area. The KQL content is scoped to what DP-600 tests, so it focuses on selecting, filtering, and aggregating data rather than advanced Kusto features. If you work heavily with real-time data pipelines, you may want supplemental practice outside this course.