DP-700 Certification

The DP-700 certification exam of Microsoft validates your ability to implement data engineering solutions using Microsoft Fabric, certifying you as a Fabric Data Engineer. 🧑‍🎓

The exam covers essential skills and key components such as warehouses, lakehouses, data pipelines, dataflows and notebooks. The key skills required for the DP-700 exam include:

Implement and manage an analytics solution (30–35%)
Ingest and transform data (30–35%)
Monitor and optimize an analytics solution (30–35%)

This page provides a comprehensive overview of the key skills covered in the DP-700 exam. We provide step-by-step tutorials to each exam topic, helping you to prepare for the DP-700 certification exam and master the skills needed to become a certified Fabric Data Engineer. 🚀

Microsoft Fabric Book

We are delighted to publish a hands-on guide to implementing end-to-end data projects in Microsoft Fabric. This hands-on book walks you through the key components and functionalities of Microsoft Fabric and invites you to actively follow the steps yourself. Numerous visual elements are used to make learning even clearer. The explanations are illustrated by a fictional story about a futuristic data factory that symbolizes Microsoft Fabric.

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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 folder/file-level access controls
Implement dynamic data masking
Apply sensitivity labels to items
Endorse items
Implement and use workspace logging

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, KQL, 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
Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence
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