A free, open study guide for the Microsoft Certified: Fabric Data Engineer Associate (DP-700) exam: revision notes for every domain, side-by-side comparisons of commonly confused services, a glossary, 20 worked sample questions, and the official syllabus as a checklist with a full free lesson for every topic.
Maintained by SaveMyCert, where you can read every lesson free, practice with explained questions and take timed mock exams.
- Exam at a glance
- Exam domains
- What is in this repo
- Syllabus checklist
- How to study for DP-700
- Sample questions
- Free resources
| Exam code | DP-700 |
| Level | Associate |
| Questions | 40–60 |
| Time limit | 100 min |
| Passing score | 700 / 1000 |
| Format | Multiple choice & more |
| Exam fee | $165 (US; varies by country) |
| Valid for | 1 year (free renewal) |
Exam details change. Always confirm them in the official Microsoft DP-700 study guide from Microsoft Learn.
| # | Domain | Weight | Topics |
|---|---|---|---|
| 1 | Implement and manage an analytics solution | 34% | 4 |
| 2 | Ingest and transform data | 33% | 3 |
| 3 | Monitor and optimize an analytics solution | 33% | 3 |
That is 3 domains and 10 topics. Spend your time in proportion to the weights: the heaviest domain decides more of your score than the lightest.
Microsoft publishes each weight as a range (for example 30–35%). The figures here fall within those ranges, adjusted to add up to 100%.
| File | What it gives you |
|---|---|
| Domain 1 notes | Implement and manage an analytics solution: condensed revision notes per topic |
| Domain 2 notes | Ingest and transform data: condensed revision notes per topic |
| Domain 3 notes | Monitor and optimize an analytics solution: condensed revision notes per topic |
| Commonly confused services | Side-by-side tables of the services questions set against each other |
| Glossary | Every in-scope term and service in one sentence |
| Sample questions | 20 worked questions with answers and reasoning |
| Exam-day guide | Booking, testing options, scoring, results and retakes |
Tick each topic off once you can explain it without notes. The "Must know" facts are the ones questions turn on. Each lesson link goes to the complete, free lesson.
Weight: 34%. Fabric workspace configuration, lifecycle management, security and governance, and process orchestration. Official weighting 30–35%.
📝 Revision notes: Domain 1: Implement and manage an analytics solution
- Configure Microsoft Fabric workspace settings
Configuring Spark workspace settings; configuring domain workspace settings; configuring OneLake workspace settings; configuring Apache Airflow workspace settings.- 📖 Lesson: Configure Microsoft Fabric Workspace Settings: Spark, Domains, OneLake, Airflow
- Must know: Tenant admins enable features and create domains; capacity admins control compute limits and whether custom pools are allowed; workspace admins configure their own workspace within those guardrails.
- Must know: Starter pools give session starts in seconds from pre-warmed nodes; custom pools give node family, size, and autoscale control but cold-start in minutes.
- Implement lifecycle management in Fabric
Configuring version control; implementing database projects; creating and configuring deployment pipelines.- 📖 Lesson: Fabric Lifecycle Management: Git Integration and Deployment Pipelines
- Must know: Git integration = source control for item definitions; deployment pipelines = staged promotion between workspaces — neither one ever moves data.
- Must know: A workspace connects to exactly one branch (Azure DevOps or GitHub) and a folder within it; only a workspace admin can connect or disconnect.
- Configure security and governance
Implementing workspace-level and item-level access controls; implementing row-level, column-level, object-level, and folder/file-level access controls; implementing dynamic data masking; applying sensitivity labels to items; endorsing items; implementing and using Microsoft Fabric audit logs; configuring and implementing OneLake security.- 📖 Lesson: Fabric Security and Governance: Workspace Roles, RLS, and OneLake Access
- Must know: Fabric security stacks three layers: workspace roles (what you can do), item permissions (which items), and data-level security (which rows, columns, and files).
- Must know: Only Admins manage the workspace and other Admins; Members can share and manage lower roles; Contributors create and edit but cannot share; Viewers read through the SQL analytics endpoint only.
- Orchestrate processes
Choosing between Dataflow Gen2, a pipeline, and a notebook; designing and implementing schedules and event-based triggers; implementing orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions.- 📖 Lesson: Fabric Orchestration: Dataflow Gen2 vs Pipeline vs Notebook, and Triggers
- Must know: Dataflow Gen2 = low-code Power Query transforms with rich connectors; pipeline = control flow, Copy activity, and orchestration; notebook = code-first Spark. They compose: pipelines invoke both.
- Must know: Pipelines orchestrate with ForEach, If/Switch, Until, Lookup, Get Metadata, Invoke Pipeline, and dependency conditions (success/failure/completion) that double as error-handling paths.
Weight: 33%. Loading patterns, batch ingestion and transformation, and streaming data processing. Official weighting 30–35%.
📝 Revision notes: Domain 2: Ingest and transform data
- Design and implement loading patterns
Designing and implementing full and incremental data loads; preparing data for loading into a dimensional model; designing and implementing a loading pattern for streaming data.- 📖 Lesson: Fabric Loading Patterns: Full, Incremental, and Dimensional Model Loads
- Must know: Full loads rebuild the target every run — simple and idempotent, right for small or change-opaque sources; incremental loads process only changed rows and are mandatory at scale.
- Must know: A watermark load filters the source on a high-water-mark column and stores the new maximum after each run — taken from the extracted data, not the clock — but it cannot detect hard deletes.
- Ingest and transform batch data
Choosing an appropriate data store; choosing between Dataflows Gen2, notebooks, KQL, and T-SQL for data transformation; creating and managing OneLake shortcuts; implementing mirroring; ingesting data by using pipelines; transforming data by using PySpark, SQL, and KQL; denormalizing data; grouping and aggregating data; handling duplicate, missing, and late-arriving data.- 📖 Lesson: Batch Ingestion in Fabric: Data Stores, Shortcuts, Mirroring, and Pipelines
- Must know: Store choice is workload plus language: Lakehouse for Spark and files, Warehouse for full T-SQL DML and multi-table transactions, Eventhouse for telemetry queried with KQL, semantic model as the BI layer — and the Lakehouse SQL analytics endpoint is read-only.
- Must know: Tool choice follows persona and scale: Dataflows Gen2 for low-code Power Query users, notebooks for large or complex code-first transforms, T-SQL for Warehouse-centric SQL teams, KQL for shaping event data in an Eventhouse.
- Ingest and transform streaming data
Choosing an appropriate streaming engine; choosing between native tables and OneLake shortcuts in Real-Time Intelligence, and between Query acceleration for OneLake shortcuts and standard OneLake shortcuts; processing data by using Eventstreams, Spark structured streaming, and KQL; creating windowing functions.- 📖 Lesson: Streaming Data in Fabric: Eventstreams, Spark, and Real-Time Intelligence
- Must know: Match the engine to the workload: Eventstream for no-code capture and routing, Spark structured streaming for code-first complex transformation into Delta tables, KQL in an Eventhouse for interactive time-series analytics, dashboards, and alerting.
- Must know: Eventstreams connect sources (Azure Event Hubs, Azure IoT Hub, CDC, custom endpoints) through no-code operators — filter, manage fields, aggregate, group by, join, union, expand — to destinations: Eventhouse, Lakehouse, derived stream, custom endpoint, or Activator.
Weight: 33%. Monitoring Fabric items, resolving errors across the stack, and performance optimization. Official weighting 30–35%.
📝 Revision notes: Domain 3: Monitor and optimize an analytics solution
- Monitor Fabric items
Monitoring data ingestion; monitoring data transformation; monitoring semantic model refresh; configuring alerts.- 📖 Lesson: Monitor Fabric Items: Monitoring Hub, Refresh History, and Activator Alerts
- Must know: The Monitoring hub is the cross-workspace triage surface for job run history — pipelines, Dataflow Gen2, notebooks, Spark job definitions, and semantic model refreshes.
- Must know: Pipeline diagnostics live at the activity level: status, input/output JSON, error codes, retries, and rerun-from-failed-activity.
- Identify and resolve errors
Identifying and resolving pipeline errors, Dataflow Gen2 errors, notebook errors, Eventhouse errors, Eventstream errors, T-SQL errors, and OneLake shortcut errors.- 📖 Lesson: Identify and Resolve Fabric Errors: Pipelines, Dataflows, Notebooks, and More
- Must know: Classify every failure into one of five classes — authentication, connectivity, schema/data, resource, or logic — and fix the class, not the symptom.
- Must know: Pipelines fail at the activity level: read the error details and evaluated input JSON, reserve retry policies for transient classes, and rerun from the failed activity.
- Optimize performance
Optimizing a Lakehouse table; optimizing a pipeline; optimizing a data warehouse; optimizing Eventstreams and Eventhouses; optimizing Spark performance; optimizing query performance.- 📖 Lesson: Optimizing Performance in Fabric: Lakehouse, Spark, Warehouse and Queries
- Must know: The small-file problem is the top Lakehouse performance killer: fix it with OPTIMIZE bin compaction, keep it fixed with scheduled maintenance, and reclaim old files with VACUUM.
- Must know: V-Order is a write-time Parquet optimization that dramatically speeds Direct Lake and SQL reads — the standard trade of slightly slower writes for much faster reads.
- Read the lesson for each topic in the checklist above, starting with the heaviest domain. Every lesson is free on the DP-700 revision notes.
- Practice straight after reading. Answer DP-700 practice questions on the topic you just read. Each option comes with an explanation of why it is right or wrong.
- Review what you got wrong, re-read that lesson section, and tick the topic off only when you get its questions right.
- Take a full-length DP-700 mock exam under the real time limit. Aim to pass mocks comfortably before you book.
- On the last day, skim the DP-700 cheat sheet instead of starting anything new.
sample-questions.md has 20 worked DP-700 questions with the answer, why each option is right or wrong, and the reasoning steps.
- Microsoft DP-700 study guide: the official source (Microsoft Learn)
- DP-700 certification overview
- DP-700 revision notes: every lesson, free to read
- DP-700 practice questions: with an explanation on every option
- DP-700 mock exams: full-length and timed
- DP-700 cheat sheet: the key facts on one page
- All certification study guides
Spotted an error or an out-of-date fact? Open an issue with the topic and a link to the official source. See CONTRIBUTING.md.
This guide is licensed under CC BY 4.0. You can reuse and adapt it, including commercially, as long as you credit SaveMyCert with a link to https://www.savemycert.com/.
This is an independent study resource. It is not affiliated with or endorsed by Microsoft Learn. Microsoft Certified: Fabric Data Engineer Associate and DP-700 are trademarks of their respective owner. Exam domains and weights are taken from the official exam guide linked above.