Free Practice Questions for Microsoft Certified: Fabric Data Engineer Associate (DP-700) Certification
- Exam guide version:
- October 19, 2026
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 17 Sep 2026
- Question bank last updated:
- 18 Sep 2026
Study with 347 exam-style practice questions designed to help you prepare for the Microsoft Certified: Fabric Data Engineer Associate (DP-700). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Key information about Microsoft Certified: Fabric Data Engineer Associate (DP-700)
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associate (intermediate)
Candidates for this exam should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. They work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics. Skills in SQL, PySpark, and KQL are essential.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Implement and manage an analytics solution
1.1: Configure Microsoft Fabric workspace settings
Configure Spark workspace settings Configure domain workspace settings Configure OneLake workspace settings Configure Apache Airflow workspace settings
1.2: Implement lifecycle management in Fabric
Configure version control Implement database projects Create and configure deployment pipelines
1.3: 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
1.4: 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
2.1: 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
2.2: 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
2.3: 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
3.1: Monitor Fabric items
Monitor data ingestion Monitor data transformation Monitor semantic model refresh Configure alerts
3.2: 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
3.3: Optimize performance
Optimize a Lakehouse table Optimize a pipeline Optimize a data warehouse Optimize Eventstream and Eventhouse Optimize Spark performance Optimize query performance
Techniques & products