DP-600 study guide
DP-600 · Work through your exam, one topic at a time.
The Microsoft Certified: Fabric Analytics Engineer Associate credential is an intermediate-level certification for professionals who design, create, and deploy enterprise-scale data analytics solutions on Microsoft Fabric. It sits at the intersection of data engineering and data analysis: candidates prepare and enrich data for analysis, secure and maintain analytics assets, and implement and manage semantic models. Day to day, that means working with lakehouses, warehouses, notebooks, dataflows, pipelines, semantic models, and reports, while applying analytics best practices such as version control and deployment. The role also requires partnership with solution architects, data engineers, data scientists, AI engineers, database administrators, and Power BI analysts, plus hands-on skill in SQL, DAX, and PySpark. Topics on this exam: Plan, implement, and manage a solution for data analytics, Prepare and serve data, Implement and manage semantic models, and Explore and analyze data. The DP-600 study guide has 4 sections and about 1,087 words.
01Plan, implement, and manage a solution for data analytics
Fabric analytics solutions begin with platform decisions that constrain everything later: capacity, workspace, and governance. A capacity (F SKU or P SKU) supplies compute; a workspace is a security and collaboration boundary that must be assigned to a capacity before most items can run.
262 words, about 1 min02Prepare and serve data
Prepare and serve data in Fabric is mostly about picking the right engine and ingestion method. A lakehouse stores files and Delta tables in OneLake and is transformed with notebooks (PySpark) or Dataflows Gen2 (Power Query).
277 words, about 1 min03Implement and manage semantic models
Semantic models in Fabric are tabular models with a storage mode. Import copies data and is fastest but requires refresh.
272 words, about 1 min04Explore and analyze data
Exploring and analyzing data in Fabric spans several surfaces. Use the SQL analytics endpoint for T-SQL over lakehouse tables, KQL querysets for real-time data, notebooks for PySpark profiling, and Data Wrangler for quick visual transforms.
276 words, about 1 min