Microsoft Fabric Data Engineer (DP-700) Exam Prep

What's inside

1. Level 1: DP-700 Exam Craft and How This Course WorksFree
   [Exam orientation] Covers how the DP-700 exam works: a score of 700 or more out of 1000 passes, and questions come as recognition, product-contrast and scenario 'best answer' items. It also makes clear that the skills-measured study guide belongs to Microsoft, is free on Microsoft Learn and is not copied here, that exam length and price are on the official exam page, and that this course is independent of Microsoft, not endorsed by it, and cannot promise a pass.
2. Level 2: Fabric and OneLake Primer for Data EngineersFree
   [Implement and manage an analytics solution] A primer for the whole syllabus: capacities, workspaces and items, OneLake as one Delta-Parquet data lake per tenant, and how the Lakehouse, Warehouse, Eventhouse, pipelines, notebooks and Dataflow Gen2 fit together.
3. Level 3: Configure Spark Workspace Settings
   [Implement and manage an analytics solution – Configure Microsoft Fabric workspace settings] Covers the Spark settings in Workspace settings > Data Engineering/Science: starter pool vs custom pools, node size and autoscale, dynamic executor allocation, Runtime version, default environment, and high concurrency mode.
4. Level 4: Configure Domain Workspace Settings
   [Implement and manage an analytics solution – Configure Microsoft Fabric workspace settings] Covers domains and subdomains in Fabric: assigning a workspace to a domain, the roles of domain admins and domain contributors, and how domains group data for data mesh discovery in the OneLake catalog.
5. Level 5: Configure OneLake Workspace Settings
   [Implement and manage an analytics solution – Configure Microsoft Fabric workspace settings] Covers the OneLake options for a workspace, such as shortcut caching and its retention period, and how they work with tenant-level OneLake settings like access from apps outside Fabric (OneLake file explorer, ADLS-compatible APIs).
6. Level 6: Configure Apache Airflow Workspace Settings
   [Implement and manage an analytics solution – Configure Microsoft Fabric workspace settings] Covers the Apache Airflow job in Fabric and its workspace-level runtime settings: the starter pool that pauses when idle, custom pools with size and autoscale for always-on production scheduling, and adding DAGs and requirements.
7. Level 7: Configure Version Control with Git Integration
   [Implement and manage an analytics solution – Implement lifecycle management in Fabric] Covers connecting a workspace to an Azure DevOps or GitHub repository and branch, committing and updating items, resolving conflicts, and using branch-out for feature development.
8. Level 8: Implement Database Projects
   [Implement and manage an analytics solution – Implement lifecycle management in Fabric] Covers managing a Fabric Warehouse schema as code with SQL database projects: building a .dacpac, comparing schemas, and deploying with VS Code or SqlPackage.
9. Level 9: Create and Configure Deployment Pipelines
   [Implement and manage an analytics solution – Implement lifecycle management in Fabric] Covers Fabric deployment pipelines: assigning workspaces to stages (for example Development, Test, Production), comparing and deploying content, deployment rules for data sources and parameters, and the permissions each stage needs.
10. Level 10: Workspace-Level and Item-Level Access Controls
    [Implement and manage an analytics solution – Configure security and governance] Covers the four workspace roles (Admin, Member, Contributor, Viewer) and item-level permissions given by sharing, such as Read, ReadData, ReadAll and Build, applying least privilege.
11. Level 11: Row, Column, Object and Folder/File Access, OneLake Security
    [Implement and manage an analytics solution – Configure security and governance] Covers fine-grained security: row-level security in T-SQL with predicate functions and security policies, column-level and object-level security with GRANT/DENY, and folder/file-level access through OneLake security roles that apply across engines.
12. Level 12: Masking, Sensitivity Labels, Endorsement and Audit Logs
    [Implement and manage an analytics solution – Configure security and governance] Covers dynamic data masking functions and the UNMASK permission, applying Microsoft Purview sensitivity labels to items, endorsing items as Promoted, Certified or Master data, and searching Microsoft Fabric audit logs.
13. Level 13: Choose a Tool; Schedules and Event-Based Triggers
    [Implement and manage an analytics solution – Orchestrate processes] Covers choosing between Dataflow Gen2 (low-code Power Query), a pipeline (orchestration and copy) and a notebook (code-first Spark), then designing schedules and event-based triggers such as OneLake or storage file events through Fabric Activator.
14. Level 14: Orchestration Patterns: Parameters and Dynamic Expressions
    [Implement and manage an analytics solution – Orchestrate processes] Covers orchestration patterns that combine notebooks and pipelines: pipeline parameters and variables, dynamic expressions such as @pipeline().parameters and @activity().output, ForEach/If Condition/Invoke pipeline activities, notebook parameter cells, and notebookutils.notebook.run/runMultiple.
15. Level 15: Design Full and Incremental Data Loads
    [Ingest and transform data – Design and implement loading patterns] Covers full and incremental data loads: watermark columns, change data capture, and upserts with Delta MERGE in PySpark and MERGE in T-SQL, plus how to handle deletes.
16. Level 16: Prepare Data for a Dimensional Model
    [Ingest and transform data – Design and implement loading patterns] Covers preparing data for loading into a dimensional model: fact and dimension tables, surrogate keys, slowly changing dimensions Type 1 and Type 2, and the unknown member for unmatched keys.
17. Level 17: Choose a Data Store and a Transformation Tool
    [Ingest and transform data – Ingest and transform batch data] Covers choosing an appropriate data store (Lakehouse, Warehouse, Eventhouse, SQL database) and choosing between Dataflows Gen2, notebooks, KQL and T-SQL for data transformation.
18. Level 18: Create and Manage OneLake Shortcuts
    [Ingest and transform data – Ingest and transform batch data] Covers OneLake shortcuts to internal OneLake locations and external sources (ADLS Gen2, Amazon S3, Google Cloud Storage, Dataverse and others): table vs file shortcuts, connection identity, and how permissions pass through.
19. Level 19: Implement Mirroring
    [Ingest and transform data – Ingest and transform batch data] Covers Fabric mirroring, which replicates operational databases such as Azure SQL Database, Azure Cosmos DB and Snowflake into OneLake as Delta tables in near real time, plus open mirroring and how mirroring compares with shortcuts and pipeline copies.
20. Level 20: Ingest Data by Using Pipelines
    [Ingest and transform data – Ingest and transform batch data] Covers ingesting data with pipelines: Copy data activity sources, sinks and connections, the on-premises data gateway, staging, table mapping, and parameterised copy into Lakehouse and Warehouse tables.
21. Level 21: Transform: Denormalize, Group and Aggregate
    [Ingest and transform data] Serves 'Ingest and transform batch data': the learner transforms data using PySpark, SQL and KQL by denormalizing data (joining dimensions onto facts) and grouping and aggregating it, and writes each pattern in all three languages side by side.
22. Level 22: Duplicate, Missing and Late-Arriving Data
    [Ingest and transform data] Serves 'Ingest and transform batch data': the learner handles duplicate, missing and late-arriving data with deduplication, null handling and upsert patterns that keep dimensional tables correct when records arrive out of order.
23. Level 23: Streaming Loading Patterns and Engine Choice
    [Ingest and transform data] Serves 'Design and implement a loading pattern for streaming data' and 'Choose an appropriate streaming engine': the learner compares Eventstream, Spark structured streaming and KQL in an Eventhouse and chooses an end-to-end streaming loading pattern from latency, volume and skill requirements.
24. Level 24: RTI: Native Tables vs Shortcuts, Query Acceleration
    [Ingest and transform data] Serves 'Choose between native tables and OneLake shortcuts in Real-Time Intelligence' and 'Choose between Query acceleration for OneLake shortcuts and standard OneLake shortcuts in Real-Time Intelligence': the learner decides when to ingest into an Eventhouse and when to query OneLake data in place.
25. Level 25: Process Data by Using Eventstream
    [Ingest and transform data] Serves 'Process data by using Eventstream': the learner builds an Eventstream with sources, event processing operations and destinations, including routing one stream to several destinations.
26. Level 26: Process Data by Using Spark Structured Streaming
    [Ingest and transform data] Serves 'Process data by using Spark structured streaming': the learner writes a PySpark streaming job in a Fabric notebook that reads a stream, transforms it and writes reliably to a Delta table.
27. Level 27: Process with KQL and Create Windowing Functions
    [Ingest and transform data] Serves 'Process data by using KQL' and 'Create windowing functions': the learner processes streaming data in an Eventhouse with update policies and materialized views, and implements tumbling, hopping, sliding and session windows across KQL, Eventstream and Spark.
28. Level 28: Monitor Data Ingestion and Transformation
    [Monitor and optimize an analytics solution] Serves 'Monitor data ingestion' and 'Monitor data transformation': the learner uses the Monitor hub, pipeline run history, notebook and Spark application monitoring, and the Capacity Metrics app to track the health of loads and transforms.
29. Level 29: Monitor Semantic Model Refresh
    [Monitor and optimize an analytics solution] Serves 'Monitor semantic model refresh': the learner monitors import and Direct Lake semantic model refreshes, reads refresh history and connects refresh to the upstream loads that feed it.
30. Level 30: Configure Alerts
    [Monitor and optimize an analytics solution] Serves 'Configure alerts': the learner configures alerts on failures and data conditions using pipeline notification activities, Activator rules and Real-Time hub events.
31. Level 31: Identify and Resolve Pipeline Errors
    [Monitor and optimize an analytics solution] Serves 'Identify and resolve pipeline errors': the learner diagnoses failed pipeline runs from activity output and error codes and fixes connection, permission, expression and timeout problems.
32. Level 32: Identify and Resolve Dataflow Gen2 Errors
    [Monitor and optimize an analytics solution] Serves 'Identify and resolve Dataflow Gen2 errors': the learner troubleshoots refresh failures, data type and destination mapping errors, gateway problems and staging issues in Dataflow Gen2.
33. Level 33: Identify and Resolve Notebook Errors
    [Monitor and optimize an analytics solution] Serves 'Identify and resolve notebook errors': the learner reads Spark stack traces and fixes session, library, memory, schema and Delta write errors in Fabric notebooks.
34. Level 34: Resolve Eventhouse and Eventstream Errors
    [Monitor and optimize an analytics solution] Serves 'Identify and resolve Eventhouse errors' and 'Identify and resolve Eventstream errors': the learner diagnoses ingestion failures, mapping and schema errors and source/destination connectivity problems in Real-Time Intelligence.
35. Level 35: Resolve T-SQL and OneLake Shortcut Errors
    [Monitor and optimize an analytics solution] Serves 'Identify and resolve T-SQL errors' and 'Identify and resolve OneLake shortcut errors': the learner fixes Warehouse and SQL analytics endpoint errors and broken or inaccessible shortcuts.
36. Level 36: Optimize a Lakehouse Table
    [Monitor and optimize an analytics solution] Serves 'Optimize a Lakehouse table': the learner keeps Delta tables fast with OPTIMIZE, V-Order, Z-ORDER, VACUUM and sensible partitioning, and knows the cost of each.
37. Level 37: Optimize a Pipeline and a Data Warehouse
    [Monitor and optimize an analytics solution] Serves 'Optimize a pipeline' and 'Optimize a data warehouse': the learner tunes Copy activity throughput and parallelism and improves Warehouse performance through statistics, data types and query design.
38. Level 38: Optimize Eventstream and Eventhouse
    [Monitor and optimize an analytics solution] Serves 'Optimize Eventstream and Eventhouse': the learner tunes streaming throughput and KQL database performance with caching, retention, partitioning and materialized views.
39. Level 39: Optimize Spark and Query Performance
    [Monitor and optimize an analytics solution] Serves 'Optimize Spark performance' and 'Optimize query performance': the learner tunes Spark sessions, pools and code, and speeds up queries in the SQL, Spark and KQL engines.
40. Level 40: Timed Mock Exam: DP-700 (50 Questions, 100 Minutes) Timed mock
    [All sections] A timed mock exam of 50 original scenario questions in 100 minutes, weighted across Implement and manage an analytics solution, Ingest and transform data, and Monitor and optimize an analytics solution, with a 70% pass mark and every distractor explained.

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The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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