DP-700 is organised into three skill areas, each weighted almost evenly. This is a plain-English summary with the official Microsoft weightings; the Microsoft Learn study guide is authoritative.
| # | Skill area | Official weight |
|---|---|---|
| 1 | Implement and manage an analytics solution | 30–35% |
| 2 | Ingest and transform data | 30–35% |
| 3 | Monitor and optimize an analytics solution | 30–35% |
1 - Implement and manage an analytics solution
Configure Microsoft Fabric workspace settings (Spark, domain, OneLake, Dataflows Gen2). Implement lifecycle management with version control, database projects and deployment pipelines. Configure security and governance: workspace-level and item-level access, row-, column-, object- and file/folder-level controls, dynamic data masking, sensitivity labels, audit logs and OneLake security. Orchestrate processes by choosing between a Dataflow Gen2, a pipeline and a notebook, and by designing schedules and event-based triggers.
2 - Ingest and transform data
Design and implement loading patterns: full and incremental loads, preparing data for a dimensional model, and streaming loads. Ingest and transform batch data by choosing an appropriate store, choosing between Dataflows Gen2, notebooks, KQL and T-SQL, creating OneLake shortcuts, implementing mirroring, and transforming with PySpark, SQL and KQL (denormalising, grouping, aggregating, and handling duplicate, missing and late-arriving data). Ingest and transform streaming data with Eventstreams, Spark structured streaming and KQL, including windowing functions.
3 - Monitor and optimize an analytics solution
Monitor Fabric items: data ingestion, data transformation and semantic-model refresh, and configure alerts. Identify and resolve errors across pipelines, Dataflows Gen2, notebooks, Eventhouses, Eventstreams, T-SQL and OneLake shortcuts. Optimise performance of Lakehouse tables, pipelines, the data warehouse, Eventstreams and Eventhouses, Spark, and queries.