DP-700 Certification
Contents
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DP-700 exam (Fabric Data Engineer) covers a broad range of skills and tools related to Microsoft Fabric. Think of components and concepts such as:
- Data lakehouse
- Data warehouse
- Eventhouse
- Notebooks
- Ingesting and transforming data
- Securing and managing an analytics solution
- Monitoring and optimizing an analytics solution
I’ve created this page with the intention to keep all the learning resources in one single place. I hope that everyone who’s preparing to take this exam will find the page useful and that will in the end help you master all the necessary skills to become a certified Fabric Data Engineer.
Table of contents
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 data workflow 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 file-level access controls
- Implement dynamic data masking
- Apply sensitivity labels to items
- Endorse items
Orchestrate processes
- Choose between 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%)
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, notebooks, and T-SQL for data transformation
- Create and manage shortcuts to data
- 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
- Process data by using eventstreams
- Process data by using Spark structured streaming
- Process data by using KQL
- Create windowing functions
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 errors
- Identify and resolve notebook errors
- Identify and resolve eventhouse errors
- Identify and resolve eventstream errors
- Identify and resolve T-SQL errors
Optimize performance
- Optimize a lakehouse table
- Optimize a pipeline
- Optimize a data warehouse
- Optimize eventstreams and eventhouses
- Optimize Spark performance
- Optimize query performance