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DP-700 Syllabus

2026: Exam Topics, Weightage and 6-Week Study Plan

The DP-700 syllabus has three equal domains: implement and manage an analytics solution, ingest and transform data, and monitor and optimize an analytics solution, each worth 30–35% of the exam.

Every skill measured on the DP-700 exam as of 19 October 2026, what each one means at work, a hands-on task to practise it, and a 6-week plan to cover the full syllabus.

3 domains 30–35% each SQL · PySpark · KQL Updated 19 Oct 2026 6-week plan
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By Dinesh, Azure Data Engineering Trainer (9+ years) · Brolly Academy · Updated 19 October 2026 · Skills measured as of 19 October 2026

DP-700 syllabus 2026 topics, weightage and 6-week study plan – Brolly Academy

Table of Contents

Quick answer

1. DP-700 syllabus at a glance

2. Domain 1: Implement and manage an analytics solution (30–35%)

3. Domain 2: Ingest and transform data (30–35%)

4. Domain 3: Monitor and optimize an analytics solution (30–35%)

5. What changed in the DP-700 syllabus on 19 October 2026

6. Skills to learn before you start the DP-700 syllabus

7. 6-week DP-700 study plan

8. Free official resources mapped to the syllabus

9. Common mistakes when studying the DP-700 syllabus

10. How Brolly Academy helps

Frequently asked questions

Sources and further reading

Quick answer

The DP-700 syllabus (Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric) has three domains, each worth 30–35% of the exam:

  1. Implement and manage an analytics solution: workspace settings, lifecycle management, security and governance, and orchestration.
  2. Ingest and transform data: loading patterns, batch ingestion and transformation, and streaming data.
  3. Monitor and optimize an analytics solution: monitoring Fabric items, fixing errors, and optimizing performance.

You are expected to work with SQL, PySpark and KQL in Microsoft Fabric. This guide lists every skill measured as of 19 October 2026, shows what each one means in practice, and gives you a 6-week study plan.

Key takeaways

  1. Three domains, 30–35% each. You can't skip any of them.
  2. You use three query languages: T-SQL, PySpark and KQL (Kusto Query Language).
  3. Many questions test choosing the right tool: pipeline, Dataflow Gen2, notebook, T-SQL or KQL.
  4. Security, Git and deployment pipelines are a large part of Domain 1, not optional extras.
  5. Plan 6–8 weeks if you know SQL and Python basics, with at least half your time hands-on in a Fabric workspace.

Want the full certification process: booking, exam day, retakes, cost and renewal? Read our Azure data engineer certification guide.

1. DP-700 syllabus at a glance

Short answer: The official study guide splits DP-700 into 3 domains and 10 skill groups. Every domain carries the same weight, so a balanced plan beats deep study of one area.

Domain Weight Skill groups Implement and manage an analytics solution 30–35% Configure workspace settings · Implement lifecycle management · Configure security and governance · Orchestrate processes Ingest and transform data 30–35% Design and implement loading patterns · Ingest and transform batch data · Ingest and transform streaming data Monitor and optimize an analytics solution 30–35% Monitor Fabric items · Identify and resolve errors · Optimize performance

Verdict: Treat each domain as one-third of your score. Most people who fail over-study Spark and under-study security, lifecycle and monitoring.

Microsoft Fabric items you must know

Before the domains make sense, know what each Fabric item does:

Fabric item What it is Where it appears in the syllabus OneLake The single, built-in data lake behind every Fabric workspace All three domains Lakehouse Delta tables and files you work with through Spark and a SQL analytics endpoint Domains 2 and 3 Warehouse A T-SQL data warehouse Domains 1, 2 and 3 Pipeline Data Factory orchestration: copy, schedule, run notebooks Domains 1, 2 and 3 Dataflow Gen2 Low-code data preparation with Power Query Domains 1, 2 and 3 Notebook PySpark or Spark SQL code Domains 1, 2 and 3 Eventstream Captures and routes real-time events Domains 2 and 3 Eventhouse Stores and queries streaming data with KQL Domains 2 and 3 Semantic model The model behind Power BI reports Domain 3 (refresh monitoring)

2. Domain 1: Implement and manage an analytics solution (30–35%)

Short answer: Domain 1 tests whether you can set up and run a Fabric environment safely: workspace settings, version control and deployment, access control and governance, and orchestrating jobs.

Configure Microsoft Fabric workspace settings

Skill measured What you must be able to do Practise this Spark workspace settings Choose pools, environments and Spark settings for a workspace Create a custom Spark environment and attach it to a notebook Domain workspace settings Assign workspaces to Fabric domains for governance Create a domain and assign a workspace to it OneLake workspace settings Configure OneLake-related settings for a workspace Review OneLake settings and shortcut options Apache Airflow workspace settings Configure settings for Apache Airflow jobs (added in the October 2026 update) Open the Airflow job settings and review the options

Implement lifecycle management in Fabric

Skill measured What you must be able to do Practise this Version control Connect a workspace to Git and commit changes Connect a workspace to a Git repo; commit a notebook change Database projects Manage Warehouse or SQL database schema as a project Create a database project from a Warehouse Deployment pipelines Promote content dev → test → prod Build a 3-stage deployment pipeline and deploy a Lakehouse

Configure security and governance

Skill measured What you must be able to do Practise this Workspace-level access controls Assign Admin, Member, Contributor and Viewer roles correctly Give a test user Viewer access and check what they can't do Item-level access controls Share single items without workspace access Share one Warehouse with read-only permission Row-, column-, object- and folder/file-level access Restrict data inside items Apply row-level security on a Warehouse table Dynamic data masking Hide sensitive column values from some users Mask an email column and test as a non-admin Sensitivity labels Classify items Apply a label to a Lakehouse Endorsement Mark items as promoted or certified Promote a semantic model Fabric audit logs Find who did what Locate audit events for an item OneLake security Control access to data in OneLake Configure a OneLake security role on Lakehouse folders

Orchestrate processes

Skill measured What you must be able to do Practise this Choose between Dataflow Gen2, a pipeline and a notebook Pick the right tool for a scenario Load the same CSV all three ways; note effort and run time Schedules and event-based triggers Run work on time or when data arrives Schedule a pipeline; add a storage-event trigger Orchestration patterns with notebooks and pipelines Use parameters and dynamic expressions Pass a date parameter from a pipeline into a notebook
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3. Domain 2: Ingest and transform data (30–35%)

Short answer: Domain 2 tests the core data engineering work: designing full and incremental loads, getting batch and streaming data into Fabric, and transforming it with PySpark, T-SQL and KQL.

Design and implement loading patterns

Skill measured What you must be able to do Practise this Full and incremental loads Load everything once, then only new or changed rows Build a watermark-based incremental load into a Delta table Prepare data for a dimensional model Shape data into facts and dimensions Build a small star schema (one fact, two dimensions) Loading patterns for streaming data Decide how streaming data lands and is stored Land events into an Eventhouse and a Lakehouse table

Ingest and transform batch data

Skill measured What you must be able to do Practise this Choose a data store Lakehouse vs Warehouse vs Eventhouse Write one paragraph per store on when you'd use it Choose between Dataflows Gen2, notebooks, KQL and T-SQL Pick the right transformation tool Do one aggregation in each tool OneLake shortcuts and mirroring Use data without copying it, or replicate a database Create a shortcut to ADLS Gen2; read it from a notebook Ingest data by using pipelines Copy data from sources Copy a SQL table into a Lakehouse with a pipeline Transform data by using PySpark, SQL and KQL Clean and reshape data Remove nulls and duplicates in PySpark; aggregate in T-SQL Denormalize, group and aggregate data Build reporting-ready tables Join orders and customers into one wide table Handle duplicate, missing and late-arriving data Keep loads correct over time Re-run a load with late rows and use MERGE to fix them

Ingest and transform streaming data

Skill measured What you must be able to do Practise this Choose a streaming engine Eventstream vs Spark Structured Streaming vs KQL Compare the three for one IoT scenario Native tables vs OneLake shortcuts in Real-Time Intelligence (with query acceleration) Choose where streaming data lives Query the same data through a native table and a shortcut Process data with Eventstream, Spark Structured Streaming and KQL Filter and transform events Route sample events through Eventstream into an Eventhouse Windowing functions Aggregate events over time windows Write a KQL query that counts events per 5 minutes

Mini examples: the three languages you need

KQL (time-window aggregation)

Events
| where Timestamp > ago(1h)
| summarize EventCount = count() by bin(Timestamp, 5m), DeviceId

PySpark (incremental load with a watermark)

last_load = spark.sql("SELECT MAX(order_date) AS d FROM sales").collect()[0]["d"]
new_rows = spark.read.table("staging_orders").filter(f"order_date > '{last_load}'")
new_rows.write.mode("append").saveAsTable("sales")

T-SQL (upsert with MERGE)

MERGE dbo.DimCustomer AS t
USING stg.Customer AS s ON t.CustomerId = s.CustomerId
WHEN MATCHED THEN UPDATE SET t.City = s.City
WHEN NOT MATCHED THEN INSERT (CustomerId, City) VALUES (s.CustomerId, s.City);

4. Domain 3: Monitor and optimize an analytics solution (30–35%)

Short answer: Domain 3 tests whether you can keep a Fabric solution healthy: watching runs and refreshes, fixing errors across every item type, and making tables, Spark jobs and queries faster.

Monitor Fabric items

Skill measured What you must be able to do Practise this Monitor data ingestion Check pipeline and copy runs Read the run history of a pipeline in the Monitor hub Monitor data transformation Check notebook and dataflow runs Find a failed notebook run and its logs Monitor semantic model refresh Check Power BI refresh status Refresh a semantic model and view its history Configure alerts Get notified when something fails Set up an alert on a failed run

Identify and resolve errors

Item Typical errors to practise Pipeline Wrong connection, missing parameter, copy activity failure Dataflow Gen2 Data type mismatch, refresh failure Notebook Missing table, schema mismatch, out-of-memory Eventhouse / Eventstream Ingestion mapping errors, missing events T-SQL Permission errors, failed MERGE OneLake shortcut Broken target path or missing permission

Optimize performance

Area What to know Lakehouse tables V-Order, OPTIMIZE (file compaction), VACUUM, partitioning Pipelines Parallel copy, avoiding unnecessary activities Data warehouse Statistics, query design, good table design Eventstream and Eventhouse Efficient KQL, retention and caching policies Spark Environment settings, avoiding small files and data skew Queries Reading query plans and reducing scanned data

5. What changed in the DP-700 syllabus on 19 October 2026

Short answer: It was a minor update. Microsoft adjusted Configure Microsoft Fabric workspace settings (Apache Airflow workspace settings now listed) and Optimize performance. The three domains and their 30–35% weights didn't change.

  • English exam: updated on 19 October 2026.
  • Other languages: updated about eight weeks after English, so check the official study guide if you take the exam in another language.
  • Practice material: anything written before October 2026 may not mention Airflow settings. Older DP-203 material doesn't cover Fabric at all.
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6. Skills to learn before you start the DP-700 syllabus

Short answer: You need working SQL, basic Python with PySpark, beginner KQL, data modelling basics and Git. Without SQL and Python, start there first.

Skill Minimum level for DP-700 Where it's tested SQL (T-SQL) Joins, window functions, CTEs, MERGE Warehouse loads, transformations, errors Python + PySpark DataFrames, read/write Delta, filter, groupBy, joins Notebooks, Lakehouse transformations, Spark tuning KQL where, summarize, bin(), ago(), joins Real-Time Intelligence, Eventhouse Data modelling Star schema, facts, dimensions, slowly changing dimensions Loading into a dimensional model Delta Lake Delta tables, OPTIMIZE, VACUUM Lakehouse tables and optimization Git Branch, commit, pull request Version control and deployment pipelines

7. 6-week DP-700 study plan

Short answer:

  • Study 10–12 hours a week for six weeks.
  • Spend at least half of that time hands-on in a Fabric workspace.
  • Cover one part of the syllabus each week, and finish with practice assessments.
Week Syllabus focus Hands-on goal Done when you can… 1 Fabric basics, OneLake, workspaces; Lakehouse vs Warehouse vs Eventhouse Create a workspace, Lakehouse and Warehouse; load a CSV Explain when to use each store 2 Batch ingestion: pipelines, Dataflow Gen2, shortcuts, mirroring Parameterised copy pipeline; OneLake shortcut Choose pipeline vs dataflow vs notebook for a scenario 3 Transformations (PySpark, T-SQL, KQL); full vs incremental loads; dimensional model Watermark incremental load; MERGE into a dimension; dedupe late data Rebuild a star schema from raw files 4 Streaming: Eventstream, Eventhouse, Spark Structured Streaming, windowing Stream sample events into an Eventhouse; KQL 5-minute window Write a windowed KQL query without notes 5 Security, governance and lifecycle Row-level security, masking, sensitivity label; Git + deployment pipeline Deploy dev → test and test access as another user 6 Monitoring, errors, optimization + revision Break and fix a pipeline and notebook; OPTIMIZE a table; practice assessments Score consistently well on the free practice assessment

A daily routine that works

  1. 30 minutes: read one Microsoft Learn module for today's topic.
  2. 60–90 minutes: build it in your Fabric workspace.
  3. 15 minutes: write three notes: what you built, what broke, and how you fixed it.
  4. Weekends: one mini project that combines the week's skills.

Fast track for DP-203 holders (about 4 weeks)

Week Focus 1 OneLake, shortcuts, mirroring, Lakehouse vs Warehouse in Fabric 2 KQL, Eventstream and Eventhouse 3 Fabric security, Git integration, deployment pipelines 4 Monitoring, optimization and practice assessments

8. Free official resources mapped to the syllabus

Short answer: Microsoft gives the study guide, learning paths, a practice assessment, an exam sandbox and a 60-day Fabric trial for free. That covers the whole DP-700 syllabus except the exam fee.

Resource Use it for DP-700 study guide The official skills list and change log. Tick off each bullet as you practise it. Microsoft Learn learning paths Free modules for each domain ("Prepare for the exam" on the certification page) Free practice assessment Checking readiness by domain in weeks 5–6 Exam sandbox Learning the question types before exam day Fabric trial 60 days of free hands-on practice with up to 1 TB in OneLake

Test yourself with our 20 free DP-700 practice questions. For exam fees, booking, retakes and renewal, see the Azure data engineer certification guide. Deciding between Fabric exams? Read DP-600 vs DP-700; for Azure Databricks, see the DP-750 guide.

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9. Common mistakes when studying the DP-700 syllabus

  1. Studying DP-203 material. It doesn't cover OneLake, shortcuts, mirroring, Eventstream or KQL.
  2. Skipping KQL. Streaming and Real-Time Intelligence appear in two of the three domains.
  3. Reading without building. "Which tool should you use?" questions need hands-on experience.
  4. Ignoring Domain 1. Security, Git and deployment pipelines are worth as much as Spark.
  5. Never breaking anything. Domain 3 is about fixing errors, so practise on purpose.

Learn the DP-700 skills hands-on

Our Azure Data Engineer Training in Hyderabad is a 2–2.5 month hands-on program in Azure Data Factory, Synapse Analytics, Databricks, PySpark, SQL and Python, with real-time projects and guidance for Microsoft certification exams. Book a free demo to review the syllabus and batch timings.

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10. How Brolly Academy helps

Our Azure Data Engineer Training in Hyderabad builds the hands-on skills behind the DP-700 syllabus:

  • Trainer: Dinesh, lead Azure Data Engineering trainer with 9+ years in cloud and data engineering
  • Duration: 2–2.5 months, with daily 3–4 hours of lab practice and mentor support
  • Tools: Azure Data Factory, Synapse Analytics, Databricks, PySpark, SQL and Python
  • Projects: real-time projects in retail analytics, IoT streaming and healthcare data
  • Modes: classroom, online and hybrid batches in English and Telugu, with a free demo class
  • Fees: from ₹9,999, with EMI options

Working on Databricks instead? See our Databricks course. Browse every course at Brolly Academy.

Frequently asked questions

What is the DP-700 syllabus?

The DP-700 syllabus has three domains: implement and manage an analytics solution, ingest and transform data, and monitor and optimize an analytics solution. Each is worth 30–35% of the exam.

What is the weightage of each DP-700 domain?

All three domains are weighted 30–35%, so each is roughly one-third of your score.

When was the DP-700 syllabus last updated?

The English skills measured were updated on 19 October 2026. It was a minor update to workspace settings (Apache Airflow) and performance optimization.

Which languages do I need for the DP-700 syllabus?

T-SQL, PySpark and KQL. You use T-SQL for warehouses, PySpark for notebooks and Lakehouse tables, and KQL for real-time data in Eventhouse.

Is KQL a big part of DP-700?

Yes. KQL is needed for Real-Time Intelligence, Eventhouse queries and windowing, which appear in the ingestion and monitoring domains.

Does the DP-700 syllabus include Power BI?

Only lightly. You may need to monitor semantic model refreshes, but building reports belongs to the DP-600 exam.

Does the DP-700 syllabus cover Azure Data Factory or Synapse?

No. DP-700 tests Data Factory pipelines and Dataflow Gen2 inside Microsoft Fabric, not standalone Azure Data Factory or Synapse Analytics.

How long does it take to cover the DP-700 syllabus?

About 6–8 weeks at 10–12 hours a week if you know SQL and Python basics. DP-203 holders can often finish in about 4 weeks.

Can I study the DP-700 syllabus for free?

Yes. The study guide, Microsoft Learn learning paths, practice assessment, exam sandbox and a 60-day Fabric trial are all free. Only the exam itself is paid.

Where can I download the official DP-700 study guide?

On Microsoft Learn at aka.ms/DP700-StudyGuide. It lists every skill measured and a change log.

Is the DP-700 syllabus the same as DP-203?

No. DP-203 covered Azure Synapse, Data Factory, Databricks and Stream Analytics. DP-700 covers Microsoft Fabric, including OneLake, shortcuts, mirroring, Eventstream, Eventhouse and KQL.

What is the difference between the DP-700 and DP-750 syllabus?

DP-700 covers Microsoft Fabric. DP-750 covers Azure Databricks, including Unity Catalog, Lakeflow Jobs and Lakeflow Spark Declarative Pipelines.

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Sources and further reading

Learn with Brolly Academy

Review the syllabus, practical projects and batch timings, and book a free demo before you enrol.

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