DP-700: Fabric Data Engineer Associate – May 2025

Skills at a glance

• Implement and manage an analytics solution (30–35%)

• Ingest and transform data (30–35%)

• Monitor and optimize an analytics solution (30–35%)

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