DP-700 study plan

Week 1chapters 1–6 · about 9 hours Done

• Read these chapters:
  • 1. Microsoft Fabric for data engineers: platform, capacities and choosing a data store
  • 2. Workspace settings for Spark, domains, OneLake and Apache Airflow
  • 3. Lifecycle management: Git, database projects and deployment pipelines
  • 4. Securing data: workspace, item, row, column, object and file access
  • 5. Governance: sensitivity labels, endorsement and audit logs
  • 6. Orchestrating with pipelines, notebooks and Dataflow Gen2
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Fabric Spark pools: starter pools vs custom pools vs capacity pools, Dynamic data masking in Fabric Warehouse: mask functions and UNMASK, Dataflow Gen2 vs pipeline vs notebook in Microsoft Fabric.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Implement and manage an analytics solution · 30–35%Ingest and transform data · 30–35%

Week 2chapters 7–10 · about 9 hours Done

• Read these chapters:
  • 7. Loading patterns: full, incremental, dimensional and streaming
  • 8. Batch ingestion: shortcuts, mirroring, pipelines and Copy job
  • 9. Transforming data with PySpark and Spark SQL
  • 10. Transforming data with T-SQL and KQL
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Full vs incremental loads in Microsoft Fabric: watermark, CDC, change data feed and mirroring, Choosing a Fabric transformation engine: Dataflow Gen2, notebooks, KQL or T-SQL.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Ingest and transform data · 30–35%

Week 3chapters 11–15 · about 8 hours Done

• Read these chapters:
  • 11. Real-Time Intelligence: engines, eventstreams and eventhouses
  • 12. Stream processing with Spark structured streaming, KQL and windowing
  • 13. Monitoring and alerts
  • 14. Identifying and resolving errors
  • 15. Optimising performance
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Choosing a Fabric streaming engine: eventstream, Spark structured streaming or eventhouse, KQL database native tables vs OneLake shortcuts vs query acceleration, Tumbling, hopping, sliding, session and snapshot windows in Fabric eventstreams.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Ingest and transform data · 30–35%Monitor and optimize an analytics solution · 30–35%

Week 4review Done

• Go back through every chapter’s key takeaways. Anything you can’t explain without looking, re-read that section.
• Re-read the common-trap callouts: they mark the answers that look right but aren’t.
• Check the exam coverage map against Microsoft’s own outline, in case it has changed since the book was checked.
• Take Microsoft’s free practice assessment, if your exam has one, from the official DP-700 study guide page. Revisit the chapters behind any weak area.
• Try the exam sandbox to get used to the exam’s screens and question formats.
• Get the free exam-day checklist and work through it a few days before your exam.

Week 1chapters 1–2 · about 3 hours Done

• Read these chapters:
  • 1. Microsoft Fabric for data engineers: platform, capacities and choosing a data store
  • 2. Workspace settings for Spark, domains, OneLake and Apache Airflow
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Fabric Spark pools: starter pools vs custom pools vs capacity pools.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Implement and manage an analytics solution · 30–35%Ingest and transform data · 30–35%

Week 2chapters 3–4 · about 4 hours Done

• Read these chapters:
  • 3. Lifecycle management: Git, database projects and deployment pipelines
  • 4. Securing data: workspace, item, row, column, object and file access
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Dynamic data masking in Fabric Warehouse: mask functions and UNMASK.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Implement and manage an analytics solution · 30–35%

Week 3chapters 5–7 · about 4 hours Done

• Read these chapters:
  • 5. Governance: sensitivity labels, endorsement and audit logs
  • 6. Orchestrating with pipelines, notebooks and Dataflow Gen2
  • 7. Loading patterns: full, incremental, dimensional and streaming
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Dataflow Gen2 vs pipeline vs notebook in Microsoft Fabric, Full vs incremental loads in Microsoft Fabric: watermark, CDC, change data feed and mirroring.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Implement and manage an analytics solution · 30–35%Ingest and transform data · 30–35%

Week 4chapter 8 · about 2 hours Done

• Read chapter:
  • 8. Batch ingestion: shortcuts, mirroring, pipelines and Copy job
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Choosing a Fabric transformation engine: Dataflow Gen2, notebooks, KQL or T-SQL.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Ingest and transform data · 30–35%

Week 5chapters 9–10 · about 4 hours Done

• Read these chapters:
  • 9. Transforming data with PySpark and Spark SQL
  • 10. Transforming data with T-SQL and KQL
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Ingest and transform data · 30–35%

Week 6chapters 11–12 · about 4 hours Done

• Read these chapters:
  • 11. Real-Time Intelligence: engines, eventstreams and eventhouses
  • 12. Stream processing with Spark structured streaming, KQL and windowing
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Choosing a Fabric streaming engine: eventstream, Spark structured streaming or eventhouse, KQL database native tables vs OneLake shortcuts vs query acceleration, Tumbling, hopping, sliding, session and snapshot windows in Fabric eventstreams.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Ingest and transform data · 30–35%

Week 7chapters 13–15 · about 4 hours Done

• Read these chapters:
  • 13. Monitoring and alerts
  • 14. Identifying and resolving errors
  • 15. Optimising performance
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Look up any unfamiliar term in the DP-700 glossary.

Exam skill areas this week: Monitor and optimize an analytics solution · 30–35%

Week 8review Done

• Go back through every chapter’s key takeaways. Anything you can’t explain without looking, re-read that section.
• Re-read the common-trap callouts: they mark the answers that look right but aren’t.
• Check the exam coverage map against Microsoft’s own outline, in case it has changed since the book was checked.
• Take Microsoft’s free practice assessment, if your exam has one, from the official DP-700 study guide page. Revisit the chapters behind any weak area.
• Try the exam sandbox to get used to the exam’s screens and question formats.
• Get the free exam-day checklist and work through it a few days before your exam.