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Exam DP-700AssociateEnglish

DP-700: Data Engineering with Microsoft Fabric

Prepares you for exam DP-700: Microsoft Fabric Data Engineer Associate

Build a medallion data platform on New York taxi data: pipelines, shortcuts and mirroring, PySpark, a dimensional warehouse, orchestration, three kinds of streaming, security and governance.

  • 42 videos
  • 8.7 h of video
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About this course

Prepare for exam DP-700, the Fabric Data Engineer Associate certification, by building one real data platform in Microsoft Fabric, end to end, in a live tenant: New York taxi data in a medallion architecture, from the first raw file to a finished dashboard.

Nineteen modules, numbered 00 to 18. Each has a theory lesson, then a lab in which every step is performed for real: 00. The trial, the tenant and the raw data 01. Workspaces and their settings 02. Choosing the data store 03. Bronze: ingest with pipelines, full and incremental 04. Shortcuts and mirroring 05. Silver: clean with PySpark and Spark SQL 06. Dataflow Gen2, and which tool for which job 07. Gold: a dimensional model in the warehouse 08. Orchestration 09. Streaming 1: Eventstream to an eventhouse 10. Streaming 2: KQL and windowing 11. Streaming 3: Spark structured streaming 12. Securing the data 13. Governance: labels, endorsement and audit logs 14. Lifecycle management 15. Monitoring and alerts 16. Troubleshooting clinic 17. Optimising 18. Capstone: the NYC Taxi Analytics dashboard

Then two videos of exam questions and answers: 48 scenario questions, each with the reasoning and the module to watch again.

Start with the Course Overview.

Practice simulation

Practise every lab in your browser

16 labs and 556 steps rebuilt from the course videos. Practise each step with voice guidance, switch on Exam mode to test yourself, or press Watch to see the lab play. The first lab is free; the rest come with a subscription.

Try the free lab

  1. Lab 0 · The trial, the tenant and the raw data Free

    4 sections · 39 steps · 12 to click

    Practise

  2. Lab 1 · Workspaces and their settings Subscribers

    5 sections · 42 steps · 20 to click

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  3. Lab 3 · Bronze: ingest with pipelines, full and incremental Subscribers

    11 sections · 117 steps · 40 to click

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  4. Lab 4 · Shortcuts and mirroring Subscribers

    8 sections · 75 steps · 19 to click

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  5. Lab 5 · Silver: clean with PySpark and Spark SQL Subscribers

    2 sections · 14 steps · 5 to click

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  6. Lab 6 · Dataflow Gen2, and which tool for which job Subscribers

    3 sections · 26 steps · 10 to click

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  7. Lab 8 · Orchestration Subscribers

    6 sections · 59 steps · 21 to click

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  8. Lab 9 · Streaming 1: Eventstream to an eventhouse Subscribers

    4 sections · 44 steps · 12 to click

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  9. Lab 10 · Streaming 2: KQL and windowing Subscribers

    2 sections · 16 steps · 2 to click

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  10. Lab 11 · Streaming 3: Spark structured streaming Subscribers

    3 sections · 25 steps · 8 to click

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  11. Lab 12 · Securing the data Subscribers

    2 sections · 19 steps · 6 to click

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  12. Lab 13 · Governance: labels, endorsement and audit logs Subscribers

    2 sections · 11 steps · 2 to click

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  13. Lab 14 · Lifecycle management Subscribers

    2 sections · 11 steps · 5 to click

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  14. Lab 15 · Monitoring and alerts Subscribers

    3 sections · 16 steps · 4 to click

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  15. Lab 16 · Troubleshooting clinic Subscribers

    2 sections · 17 steps · 3 to click

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  16. Lab 18 · Capstone: the NYC Taxi Analytics dashboard Subscribers

    4 sections · 25 steps · 4 to click

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Do it, then prove it

Hands-on labs16 Parts · 63 labs with every step, the proof that it worked, cost and clean-up. First Part free.Mock exam48 exam-style questions across 2 domains, scored by domain, each answer explained. Free 10-question sample.
Leader brief

What a technology leader should know about DP-700

What it is

Data engineering in Microsoft Fabric builds the data platform behind analytics: ingestion, a medallion architecture (bronze, silver, gold), streaming, security, governance, monitoring and optimisation.

Why now

Data teams are consolidating pipelines, Spark and warehouses onto one platform, and real-time data is becoming a normal requirement.

Decisions you will make

  • Pipelines, Dataflow Gen2 or notebooks for each job
  • Shortcuts and mirroring versus copying data
  • Batch versus streaming for each source
  • How data is secured, labelled and monitored

Risks if ignored

  • Duplicate copies of data drifting apart
  • Pipeline failures nobody notices
  • Sensitive data without labels or audit
  • Slow, costly queries

Cost

Free: The free Fabric trial capacity covers the labs.

Paid: Production needs a paid Fabric capacity.

Who owns what

Data engineerPipelines, notebooks, medallion layers
Platform adminCapacity, workspaces, monitoring
GovernanceLabels, endorsement, audit logs

Questions to ask your team

  • How do we know last night’s load succeeded?
  • Where do copies of the same data live?
  • Which data needs real time, and which does not?
Course videos

All 42 videos, in order

Playlist on YouTube
  1. 110:31Course OverviewOverview
  2. 212:41Foundation: The Medallion ArchitectureHands-on
  3. 313:00Module 00 Theory: The trial, the tenant and the raw dataTheory
  4. 412:51Module 00 Lab: The trial, the tenant and the raw dataHands-on
  5. 59:39Module 01 Theory: Workspaces and their settingsTheory
  6. 610:43Module 01 Lab: Workspaces and their settingsHands-on
  7. 710:19Module 02 Theory: Choosing the data storeTheory
  8. 84:57Module 02 Lab: Choosing the data storeHands-on
  9. 910:56Module 03 Theory: Bronze: ingest with pipelines, full and incrementalTheory
  10. 1051:43Module 03 Lab: Bronze: ingest with pipelines, full and incrementalHands-on
  11. 119:29Module 04 Theory: Shortcuts and mirroringTheory
  12. 1226:34Module 04 Lab: Shortcuts and mirroringHands-on
  13. 137:11Module 05 Theory: Silver: clean with PySpark and Spark SQLTheory
  14. 1417:03Module 05 Lab: Silver: clean with PySpark and Spark SQLHands-on
  15. 156:21Module 06 Theory: Dataflow Gen2, and which tool for which jobTheory
  16. 1611:57Module 06 Lab: Dataflow Gen2, and which tool for which jobHands-on
  17. 177:13Module 07 Theory: Gold: a dimensional model in the warehouseTheory
  18. 1817:46Module 07 Lab: Gold: a dimensional model in the warehouseHands-on
  19. 197:05Module 08 Theory: OrchestrationTheory
  20. 2019:09Module 08 Lab: OrchestrationHands-on
  21. 216:49Module 09 Theory: Streaming 1: Eventstream to an eventhouseTheory
  22. 2213:17Module 09 Lab: Streaming 1: Eventstream to an eventhouseHands-on
  23. 237:06Module 10 Theory: Streaming 2: KQL and windowingTheory
  24. 2412:41Module 10 Lab: Streaming 2: KQL and windowingHands-on
  25. 256:31Module 11 Theory: Streaming 3: Spark structured streamingTheory
  26. 2610:11Module 11 Lab: Streaming 3: Spark structured streamingHands-on
  27. 276:56Module 12 Theory: Securing the dataTheory
  28. 2818:01Module 12 Lab: Securing the dataHands-on
  29. 296:35Module 13 Theory: Governance: labels, endorsement and audit logsTheory
  30. 307:03Module 13 Lab: Governance: labels, endorsement and audit logsHands-on
  31. 316:57Module 14 Theory: Lifecycle managementTheory
  32. 326:31Module 14 Lab: Lifecycle managementHands-on
  33. 337:00Module 15 Theory: Monitoring and alertsTheory
  34. 3411:54Module 15 Lab: Monitoring and alertsHands-on
  35. 357:23Module 16 Theory: Troubleshooting clinicTheory
  36. 3611:31Module 16 Lab: Troubleshooting clinicHands-on
  37. 377:30Module 17 Theory: OptimisingTheory
  38. 389:25Module 17 Lab: OptimisingHands-on
  39. 396:16Module 18 Theory: Capstone: the NYC Taxi Analytics dashboardTheory
  40. 4015:43Module 18 Lab: Capstone: the NYC Taxi Analytics dashboardHands-on
  41. 4130:56DP-700 Exam Questions and Answers - Part 1Exam Q&A
  42. 4229:23DP-700 Exam Questions and Answers - Part 2Exam Q&A
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