DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric
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Interactive Study Guide
Each module covers one exam topic with plain-English explanations, real-world scenarios, and built-in practice. Everything you need to understand and retain the material — no tab-switching required.
Domain 1: Implement and Manage an Analytics Solution ›
Domain 2: Ingest and Transform Data ›
Domain 3: Monitor and Optimize an Analytics Solution ›
Exam Resources
Official learning paths, exam details, skills measured, and community resources to supplement your study.
Exam Quick Facts
| Detail | Value |
|---|---|
| Exam Code | DP-700 |
| Title | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Level | Associate |
| Pass Score | 700 / 1000 |
| Duration | 100 minutes |
| Questions | ~40-60 |
| Cost | $165 USD (varies by region) |
| Scheduling | Pearson VUE |
| Skills Updated | October 19, 2026 |
Study Resources
| Resource | Link |
|---|---|
| Official Exam Page | Microsoft Learn — DP-700 |
| Official Study Guide | Microsoft Study Guide |
| Free Practice Assessment | Start Practice Assessment |
| Exam Sandbox | Try the exam interface |
Skills at a Glance
| Skill Area | Weight |
|---|---|
| Implement and manage an analytics solution | 30-35% |
| Ingest and transform data | 30-35% |
| Monitor and optimize an analytics solution | 30-35% |
Who is this exam for?
This Microsoft Data certification covers data concepts and Azure data services. It tests your ability to work with relational and non-relational databases, analytics workloads, and data platforms on Azure. This is an associate-level exam that expects hands-on experience. You should have practical knowledge of the technologies covered.
Skills Measured
Implement and manage an analytics solution (30–35%)
This domain covers data visualisation and analysis. You need to know how to create effective reports, dashboards, and analytical insights.
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
Ingest and transform data (30–35%)
This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.
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
Monitor and optimize an analytics solution (30–35%)
This domain covers monitoring and maintenance. You need to know how to use monitoring tools, configure alerts, and implement backup and recovery solutions.
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
Quick Links
Compare DP-700 across AWS & Google Cloud → Cert Compass