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DP-600 In preparation

Fabric Analytics Engineer

DP-600 sits between analyst and engineer: preparing data in lakehouses and warehouses, querying it with SQL, KQL and DAX, and designing semantic models that stay fast at enterprise scale.

Microsoft Certified: Fabric Analytics Engineer Associate · for analytics engineers who build semantic models, warehouses and lakehouses

Level
Associate
Exam time
100 minutes
Passing score
700 of 1000
Microsoft Learn during the exam
Allowed
Skills outline
October 19, 2026

Study cards and questions for DP-600 are being written.

The official skills outline is below, so you can already see what the exam covers. Until this exam is ready, the best free preparation is the learning path on Microsoft Learn.

DP-600 on Microsoft Learn ↗

Skills measured

What the exam covers

The domains and objectives as Microsoft lists them in the official study guide, version of October 19, 2026. The percentage is the share of the exam.

1 Maintain a data analytics solution

25-30%

Implement security and governance

  • Implement workspace-level access controls
  • Implement item-level access controls
  • Implement row-level, column-level, object-level, and file-level access control
  • Apply sensitivity labels to items
  • Endorse items

Maintain the analytics development lifecycle

  • Configure version control for a workspace
  • Create and manage a Power BI Desktop project (.pbip)
  • Create and configure deployment pipelines
  • Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models
  • Deploy and manage semantic models by using the XMLA endpoint
  • Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

2 Prepare data

45-50%

Get data

  • Create a data connection
  • Discover data by using OneLake catalog and Real-Time hub
  • Ingest or access data as needed
  • Choose between different data stores
  • Implement OneLake integration for Eventhouse and semantic models

Transform data

  • Create views, functions, and stored procedures
  • Enrich data by adding new columns or tables
  • Implement a star schema for a lakehouse or warehouse
  • Denormalize data
  • Aggregate data
  • Merge or join data
  • Identify and resolve duplicate data, missing data, or null values
  • Convert column data types
  • Filter data

Query and analyze data

  • Select, filter, and aggregate data by using the Visual query editor
  • Select, filter, and aggregate data by using SQL
  • Select, filter, and aggregate data by using KQL
  • Select, filter, and aggregate data by using DAX

3 Implement and manage semantic models

25-30%

Design and build semantic models

  • Choose a storage mode
  • Implement a star schema for a semantic model
  • Implement relationships, such as bridge tables and many-to-many relationships
  • Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
  • Implement calculation groups, dynamic format strings, and field parameters
  • Identify use cases for and configure large semantic model storage format
  • Design and build composite models

Optimize enterprise-scale semantic models

  • Implement performance improvements in queries and report visuals
  • Improve DAX performance
  • Configure Direct Lake, including default fallback and refresh behavior
  • Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint
  • Implement incremental refresh for semantic models