Microsoft Fabric Analytics Engineer AssociateStudy guide
The associate certification for implementing analytics solutions with Microsoft Fabric (DP-600).
About Microsoft Fabric Analytics Engineer Associate (DP-600)
Microsoft Fabric Analytics Engineer Associate (DP-600) is a Associate-level certification from Microsoft. This page organizes the exam scope into a 3-chapter, 7-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Plan, implement, and manage a solution for data analytics~13%
- Prepare and serve data~42%
- Implement and manage semantic models~23%
- Explore and analyze data~22%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600
1Plan, Implement, and Manage a Solution for Data Analytics
- 1.1Planning Analytics Solutions and Fabric Setup
Understand Microsoft Fabric from an analytics-engineer view, plus workspaces/capacity and planning analytics solutions (requirements → store choice → serving). The starting point for DP-600.
- 1.2Governance and Deployment (CI/CD)
Understand Fabric workspace roles, item/data permissions, sensitivity labels and Purview, plus deployment pipelines and Git integration for CI/CD. Operate analytics assets securely and repeatably.
- 1.3Preparing Data (Ingest and Transform)
Understand the basics of DP-600’s largest domain "prepare and serve data"—ingestion (pipelines, dataflows, shortcuts) and transformation (Spark, T-SQL, medallion), plus Delta table optimization.
2Prepare and Serve Data (Modeling and Serving)
- 2.1Dimensional Modeling and Star Schema
Understand analysis-friendly data design—star schema (facts and dimensions), Slowly Changing Dimensions (SCD), and surrogate keys—key preparation that drives serving (semantic model) quality.
- 2.2Serving Data (SQL Analytics Endpoint and Direct Lake)
Understand how to serve prepared data for analysis—the SQL analytics endpoint (T-SQL queries), Power BI Direct Lake, and sharing/access—bridging the prepared gold layer to semantic models.
3Semantic Models and Exploring/Analyzing Data
- 3.1Semantic Models and DAX
Understand the Power BI semantic model (formerly dataset), relationships, measures (DAX), and the Direct Lake storage mode. Implement and optimize the "meaning layer" of analytics.
- 3.2Exploring and Analyzing Data
Understand ways to explore/analyze data—DAX queries, T-SQL, KQL, Power BI visual exploration, and performance troubleshooting (Performance Analyzer, DAX Studio).