DP-700 In preparation
Fabric Data Engineer
DP-700 covers data engineering in Microsoft Fabric: loading patterns for batch and streaming data, orchestration, security and governance, and finding and fixing what is slow or broken.
Microsoft Certified: Fabric Data Engineer Associate · for data engineers who build pipelines in Microsoft Fabric
- 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-700 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-700 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 Implement and manage an analytics solution
30-35%Configure Microsoft Fabric workspace settings
- Configure Spark workspace settings
- Configure domain workspace settings
- Configure OneLake workspace settings
- Configure Apache Airflow workspace settings
Implement lifecycle management in Fabric
- Configure version control
- Implement database projects
- Create and configure deployment pipelines
Configure security and governance
- Implement workspace-level access controls
- Implement item-level access controls
- Implement row-level, column-level, object-level, and folder/file-level access controls
- Implement dynamic data masking
- Apply sensitivity labels to items
- Endorse items
- Implement and use Microsoft Fabric audit logs
- Configure and implement OneLake security
Orchestrate processes
- Choose between Dataflow gen 2, a pipeline and a notebook
- Design and implement schedules and event-based triggers
- Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions
2 Ingest and transform data
30-35%Design and implement loading patterns
- Design and implement full and incremental data loads
- Prepare data for loading into a dimensional model
- Design and implement a loading pattern for streaming data
Ingest and transform batch data
- Choose an appropriate data store
- Choose between Dataflows Gen2, notebooks, KQL, and T-SQL for data transformation
- Create and manage OneLake shortcuts
- Implement mirroring
- Ingest data by using pipelines
- Transform data by using PySpark, SQL, and KQL
- Denormalize data
- Group and aggregate data
- Handle duplicate, missing, and late-arriving data
Ingest and transform streaming data
- Choose an appropriate streaming engine
- Choose between native tables and OneLake shortcuts in Real-Time Intelligence
- Choose between Query acceleration for OneLake shortcuts and standard OneLake shortcuts in Real-Time Intelligence
- Process data by using Eventstream
- Process data by using Spark structured streaming
- Process data by using KQL
- Create windowing functions
3 Monitor and optimize an analytics solution
30-35%Monitor Fabric items
- Monitor data ingestion
- Monitor data transformation
- Monitor semantic model refresh
- Configure alerts
Identify and resolve errors
- Identify and resolve pipeline errors
- Identify and resolve Dataflow Gen2 errors
- Identify and resolve notebook errors
- Identify and resolve Eventhouse errors
- Identify and resolve Eventstream errors
- Identify and resolve T-SQL errors
- Identify and resolve OneLake shortcut errors
Optimize performance
- Optimize a Lakehouse table
- Optimize a pipeline
- Optimize a data warehouse
- Optimize Eventstream and Eventhouse
- Optimize Spark performance
- Optimize query performance